\n", + " | publication_number | \n", + "sequence_id | \n", + "tokens | \n", + "
---|---|---|---|
0 | \n", + "US-4444749-A | \n", + "0 | \n", + "A shampoo comprising an aqueous solution of an... | \n", + "
1 | \n", + "US-4444749-A | \n", + "1 | \n", + "A shampoo comprising an aqueous solution of an... | \n", + "
DatasetRecords: The provided batch size 256 was normalized. Using value 149.\n", + "\n" + ], + "text/plain": [ + "DatasetRecords: The provided batch size \u001b[1;36m256\u001b[0m was normalized. Using value \u001b[1;36m149\u001b[0m.\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sending records...: 100%|██████████| 1/1 [00:00<00:00, 1.71batch/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "DatasetRecords(Dataset(id=UUID('a187cdad-175e-4d87-989f-a529b9999bde') inserted_at=datetime.datetime(2024, 7, 28, 7, 23, 59, 902685) updated_at=datetime.datetime(2024, 7, 28, 7, 24, 1, 901701) name='claim_tokens' status='ready' guidelines='Classify individual tokens according to the specified categories, ensuring that any overlapping or nested entities are accurately captured.' allow_extra_metadata=False workspace_id=UUID('fd4fc24c-fc1f-4ffe-af41-d569432d6b50') last_activity_at=datetime.datetime(2024, 7, 28, 7, 24, 1, 901701) url=None))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## We upload a csv with three columns : tokens, publication_number, sequence_id\n", + "\n", + "publication_df = pd.read_csv(\"/content/sample_publications.csv\")\n", + "## Convert dataframe rows to Argilla Records\n", + "records = [\n", + " rg.Record(\n", + " fields=\n", + " {\"tokens\": \"\".join(row[\"tokens\"])\n", + " ,'document_id':str(row['publication_number'])\n", + " ,'sentence_id':str(row['sequence_id'])\n", + " })\n", + " for _,row in publication_df.iterrows()\n", + " ]\n", + " ## Store Argilla records to Argilla Dataset\n", + "rg_dataset.records.log(records)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IYpb2F8SvyHL" + }, + "source": [ + "Once, we have records pushed to Argilla Dataset, the UI will render the records and the labels for the annotator to annotate the text.\n", + "\n", + "Check the screeshots below." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 378 + }, + "id": "rvgq_GcCUATV", + "outputId": "05658172-fd94-4f82-dadb-58d1d4fc2e8a" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
Sample Train Tokens:['The', 'FINFET', 'of', 'claim', '11', ',', 'wherein', 'the', 'conformal', 'gate', 'dielectric', 'comprises', 'a', 'high-κ', 'gate', 'dielectric', 'selected', 'from', 'the', 'group', 'consisting', 'of:', 'hafnium', 'oxide', '(HfO', '2', '),', 'lanthanum', 'oxide', '(La', '2', 'O', '3', '),', 'and', 'combinations', 'thereof.']
Sample Valid Tokens:['The', 'method', 'of', 'claim', '2', ',', 'wherein', 'generating', 'the', 'one', 'or', 'more', 'possible', 'design', 'modification', 'solutions', 'based', 'at', 'least', 'in', 'part', 'on', 'the', 'set', 'of', 'attack', 'mitigation', 'rules', 'comprises', 'generating', 'the', 'one', 'or', 'more', 'possible', 'design', 'modification', 'solutions', 'by', 'inputting', 'the', 'set', 'of', 'attack', 'mitigation', 'rules', 'to', 'a', 'model', 'configured', 'to', 'perform', 'structural', 'and', 'functional', 'analysis', 'to', 'interpret', 'the', 'set', 'of', 'attack', 'mitigation', 'rules,', 'wherein', 'the', 'set', 'of', 'attack', 'mitigation', 'rules', 'comprises', 'one', 'or', 'more', 'rules', 'used', 'by', 'the', 'model', 'to', 'identify', 'the', 'key-gate', 'type', 'for', 'each', 'possible', 'design', 'modification', 'solution', 'of', 'the', 'one', 'or', 'more', 'possible', 'design', 'modification', 'solutions', 'and', 'one', 'or', 'more', 'rules', 'used', 'by', 'the', 'model', 'to', 'identify', 'the', 'location', 'where', 'to', 'insert', 'the', 'key-gate', 'type', 'for', 'each', 'possible', 'design', 'modification', 'solution', 'of', 'the', 'one', 'or', 'more', 'possible', 'design', 'modification', 'solutions.']
Sample Train tags:['O', 'B-Electrical Circuit', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Chemical Compound', 'I-Chemical Compound', 'O', 'O', 'O', 'B-Chemical Compound', 'I-Chemical Compound', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
Sample Valid tags:['O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Process', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Process', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Algorithm', 'I-Algorithm', 'I-Algorithm', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Biotechnology', 'I-Biotechnology', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Process', 'I-Process', 'O', 'O']
"
+ ],
+ "text/plain": [
+ "Sample Train Tokens:\" +\n",
+ " f\"{train_tokens[0]}
Sample Valid Tokens:\" +\n",
+ " f\"{validation_tokens[0]}
Sample Train tags:\" +\n",
+ " f\"{train_ner_tags[0]}
Sample Valid tags:\" +\n",
+ " f\"{validation_ner_tags[0]}
\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OfiwXotz2Dxh"
+ },
+ "source": [
+ "As we are trying to have our data creation and model training pipeline working, for simplicity , I have dealing with one record each for training and validation."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xvdsvQgh2Fv4"
+ },
+ "source": [
+ "#### Step 4: Map labels (tags) to integers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "id": "wPqptNmRHOcB"
+ },
+ "outputs": [],
+ "source": [
+ "def mapped_ner_tags(ner_tags: List[List[str]]) -> List[List[int]]:\n",
+ " \"\"\"\n",
+ " Convert a list of NER tags to their corresponding integer IDs.\n",
+ " This function takes a list of lists containing string NER tags, creates a unique mapping\n",
+ " of these tags to integer IDs, and then converts all tags to their respective IDs.\n",
+ " Args:\n",
+ " ner_tags (List[List[str]]): A list of lists, where each inner list contains string NER tags.\n",
+ " Returns:\n",
+ " List[List[int]]: A list of lists, where each inner list contains integer IDs\n",
+ " corresponding to the input NER tags.\n",
+ " Example:\n",
+ " >>> ner_tags = [['O', 'B-PER', 'I-PER'], ['O', 'B-ORG']]\n",
+ " >>> mapped_ner_tags(ner_tags)\n",
+ " [[0, 1, 2], [0, 3]]\n",
+ " Note:\n",
+ " The mapping of tags to IDs is created based on the unique tags present in the input.\n",
+ " The order of ID assignment may vary between function calls if the input changes.\n",
+ " \"\"\"\n",
+ " labels = list(set([item for sublist in ner_tags for item in sublist]))\n",
+ " id2label = {i: label for i, label in enumerate(labels)}\n",
+ " label2id = {label: id_ for id_, label in id2label.items()}\n",
+ " mapped_ner_tags = [[label2id[label] for label in ner_tag] for ner_tag in ner_tags]\n",
+ " return mapped_ner_tags"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "id": "Ge9nS_PpAvPC"
+ },
+ "outputs": [],
+ "source": [
+ "def get_labels(ner_tags: List[List[str]]) -> List[str]:\n",
+ " \"\"\"\n",
+ " Extract unique labels from a list of NER tag sequences.\n",
+ " This function takes a list of lists containing NER tags and returns a list of unique labels\n",
+ " found across all sequences.\n",
+ "\n",
+ " Args:\n",
+ " ner_tags (List[List[str]]): A list of lists, where each inner list contains string NER tags.\n",
+ " Returns:\n",
+ " List[str]: A list of unique NER labels found in the input sequences.\n",
+ " Example:\n",
+ " >>> ner_tags = [['O', 'B-PER', 'I-PER'], ['O', 'B-ORG', 'I-ORG'], ['O', 'B-PER']]\n",
+ " >>> get_labels(ner_tags)\n",
+ " ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG']\n",
+ " Note:\n",
+ " The order of labels in the output list is not guaranteed to be consistent\n",
+ " between function calls, as it depends on the order of iteration over the set.\n",
+ " \"\"\"\n",
+ " return list(set([item for sublist in ner_tags for item in sublist]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KEx_Nb8v2MeY"
+ },
+ "source": [
+ "#### Step 5: Argilla Dataset to HuggingFace Dataset\n",
+ "We now have our data in a structure as required for token classification dataset. We will just have to create a Hugging Face Dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "id": "hH4c2lZ5HWZT"
+ },
+ "outputs": [],
+ "source": [
+ "train_labels = get_labels(train_ner_tags)\n",
+ "validation_labels = get_labels(validation_ner_tags)\n",
+ "labels = list(set(train_labels + validation_labels))\n",
+ "features = Features({\n",
+ " \"tokens\": Sequence(Value(\"string\")),\n",
+ " \"ner_tags\": Sequence(ClassLabel(num_classes=len(labels), names=labels))\n",
+ "})\n",
+ "train_records = [\n",
+ " {\n",
+ " \"tokens\": token,\n",
+ " \"ner_tags\": ner_tag,\n",
+ " }\n",
+ " for token, ner_tag in zip(train_tokens, mapped_ner_tags(train_ner_tags))\n",
+ "]\n",
+ "validation_records = [\n",
+ " {\n",
+ " \"tokens\": token,\n",
+ " \"ner_tags\": ner_tag,\n",
+ " }\n",
+ " for token, ner_tag in zip(validation_tokens, mapped_ner_tags(validation_ner_tags))\n",
+ "]\n",
+ "span_dataset = DatasetDict(\n",
+ " {\n",
+ " \"train\": Dataset.from_list(train_records,features=features),\n",
+ " \"validation\": Dataset.from_list(validation_records,features=features),\n",
+ " }\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "id": "yHfgcVTyHZh1"
+ },
+ "outputs": [],
+ "source": [
+ "# assertion to verify if train split conforms the dataset structure required for fine-tuning.\n",
+ "assert span_dataset['train'].features['ner_tags'].feature.names is not None"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2PKgAnGY2Wxo"
+ },
+ "source": [
+ "#### Step 6: Push dataset to Hugginface Hub"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "emltkVVMHdAm",
+ "outputId": "b98ab8d6-5516-45b9-8cfa-c86f45e630c5"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
+ " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
+ " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
+ " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
+ " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
+ "\n",
+ " To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
+ "Enter your token (input will not be visible): \n",
+ "Add token as git credential? (Y/n) n\n",
+ "Token is valid (permission: write).\n",
+ "Your token has been saved to /root/.cache/huggingface/token\n",
+ "Login successful\n"
+ ]
+ }
+ ],
+ "source": [
+ "!huggingface-cli login"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 202,
+ "referenced_widgets": [
+ "e4f9cff7519b401a9db04f247b7e473c",
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+ "b01ba70e26da4b83b096c1b23da688d4",
+ "35b2a6e0f8c543c4995e72d1543c39aa"
+ ]
+ },
+ "id": "mKVJEUhmHjA2",
+ "outputId": "1ebb4f40-25f5-4168-87f9-f9381cd92fb6"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e4f9cff7519b401a9db04f247b7e473c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Uploading the dataset shards: 0%| | 0/1 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "13636a22e24f4b1287bb4234f15f6a32",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Creating parquet from Arrow format: 0%| | 0/1 [00:00, ?ba/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "eec3edc793a84abeb1af22221b24b3c8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Uploading the dataset shards: 0%| | 0/1 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7ec8f069212f455589812568d35f7cde",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Creating parquet from Arrow format: 0%| | 0/1 [00:00, ?ba/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "CommitInfo(commit_url='https://huggingface.co/datasets/bikashpatra/sample_claims_annotated_hf/commit/e9faaa35dda423fcb2bccde9f19cbacd832af80a', commit_message='Upload dataset', commit_description='', oid='e9faaa35dda423fcb2bccde9f19cbacd832af80a', pr_url=None, pr_revision=None, pr_num=None)"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "span_dataset.push_to_hub(\"bikashpatra/sample_claims_annotated_hf\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vjLZrBL62myl"
+ },
+ "source": [
+ "## 5. Model Fine-tuning using AutoTrain\n",
+ "Huggingface [AutoTrain](https://huggingface.co/autotrain) is a simple tool to train model without writing a any code. We can use autotrain to fine-tune for a range of tasks like token-classification, text-generation, Image Classification and many more. In order to use AutoTrain, we will have to first create an instance of AutoTrain in HF space. Use the [create space](https://huggingface.co/new-space?template=autotrain-projects%2Fautotrain-advanced) link. For space SDK choose Docker and select AutoTrain as Docker template. We need to choose a hardware to train our model. Check the screenshots for a quick reference\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "pFPOmBoYUk1Z",
+ "outputId": "05877b09-c000-4b27-f46f-8d9994c3f11a"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "DatasetRecords: The provided batch size 256 was normalized. Using value 10.\n", + "\n" + ], + "text/plain": [ + "DatasetRecords: The provided batch size \u001b[1;36m256\u001b[0m was normalized. Using value \u001b[1;36m10\u001b[0m.\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sending records...: 100%|██████████| 1/1 [00:00<00:00, 1.15batch/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "DatasetRecords(Dataset(id=UUID('a187cdad-175e-4d87-989f-a529b9999bde') inserted_at=datetime.datetime(2024, 7, 28, 7, 23, 59, 902685) updated_at=datetime.datetime(2024, 7, 28, 7, 35, 55, 80617) name='claim_tokens' status='ready' guidelines='Classify individual tokens according to the specified categories, ensuring that any overlapping or nested entities are accurately captured.' allow_extra_metadata=False distribution=None workspace_id=UUID('fd4fc24c-fc1f-4ffe-af41-d569432d6b50') last_activity_at=datetime.datetime(2024, 7, 28, 7, 35, 55, 80181)))" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rg_dataset.records.log(records=updated_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3fuExqyYK2yx" + }, + "source": [ + "The records we update here are stored as [`suggestions`](https://docs.argilla.io/latest/reference/argilla/records/suggestions/) and not [`responses`](https://docs.argilla.io/latest/reference/argilla/records/responses/). Responses in the context of this tutorial are created when annotator saves a annotation.Suggestions are labels predicted by model.Therefore, the records we updated here will have `response.status` as `pending` and not `submitted`. This will allow us/annotators to check the predicted labels and accept or reject model predictions.\n", + "\n", + "If we want to accept model predicted annotations for tokens in a text, we may save the [`suggestions`] as [`responses`], else we will have to add / remove / edit labels applied to tokens." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r96xrhjeASAG" + }, + "source": [ + "## 8. Conclusion\n", + "\n", + "In this comprehensive tutorial, we've explored a complete workflow for data annotation and model fine-tuning. We began by setting up an [Argilla](https://argilla.io/) instance on [Hugging Face Spaces](https://huggingface.co/spaces), providing a robust platform for data management. We then configured and created a dataset within our Argilla instance, leveraging its user-friendly interface to manually annotate a subset of records.\n", + "\n", + "We continued as we exported the high-quality annotated data to a Hugging Face [dataset](https://huggingface.co/datasets), bridging the gap between annotation and model training. We then demonstrated the power of transfer learning by fine-tuning a `distilbert-base-uncased` model on this curated dataset using Hugging Face's [AutoTrain](https://huggingface.co/autotrain), a tool that simplifies the complexities of model training.\n", + "\n", + "The workflow came full circle as we applied our fine-tuned model to annotate the remaining unlabeled records in the Argilla dataset, showcasing how machine learning can accelerate the annotation process. This tutorial should provide a solid foundation for implementing an iterative annotation and fine-tuning pipeline while illustrating the synergy between human expertise and machine learning capabilities.\n", + "\n", + "This iterative approach allows for continuous improvement, making it an invaluable tool for tackling a wide range of natural language processing tasks efficiently and effectively." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "99YxP5sKGjxN" + }, + "source": [ + "## 9. Acknowledgements\n", + "\n", + "I would like to express my sincere gratitude to the following individuals who have contributed to this notebook:\n", + "\n", + "- **[David Berenstein](https://x.com/davidberenstei)** for his invaluable insights and guidance.\n", + "- **[Sara Han](https://x.com/sdiazlor)** for answering my frequent queries on discord.\n", + "\n", + "This work would not have been possible without their support and expertise.\n", + "\n", + "Additionally, a nicer version of this notebook can be seen by replacing **github** in `https://github.com/bikash119/argilla/blob/argilla_with_autotrain/argilla/docs/community/token_classification_tutorial.ipynb` with **nbsanity**. Thanks to **[Hamel Hussain](https://x.com/HamelHusain)** for creating the notebook rendering utility." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "VLwsHGv1H7qn", + "Gv3ctU0RcgcZ", + "txIoyMIBcgWq", + "aWU2pWV1cgT5", + "ZlM-G2oWcgOQ", + "gNCS-BwJx_XM", + "vjLZrBL62myl", + "E0z1Z237gUgb", + "QeuBwT8BobXm", + "r96xrhjeASAG" + ], + "gpuType": "T4", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/argilla/docs/community/token_classification_tutorial.md b/argilla/docs/community/token_classification_tutorial.md new file mode 100644 index 0000000000..dc8a9c6b1c --- /dev/null +++ b/argilla/docs/community/token_classification_tutorial.md @@ -0,0 +1,914 @@ + + +# Fine-tuning a token classification model using custom Argilla Dataset and HuggingFace AutoTrain + +We all would want to try out to solve some use case with a neat tool / techs available out there. +In this tutorial , I want to go over my learning journey to fine tune a model on US Patent text. + +## 1. Introduction + + + +### 1.1 Background on Named Entity Recognition (NER) + +Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that involves identifying and classifying named entities in text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, etc. + +### 1.2 Importance of NER in Natural Language Processing + +NER plays a crucial role in various NLP applications, including: +- Information Retrieval +- Question Answering Systems +- Machine Translation +- Text Summarization +- Sentiment Analysis + +### 1.3 Challenges in NER for Specific Domains or Languages + +While general-purpose NER models exist, they often fall short when applied to specialized domains or less-common languages due to: +- Domain-specific terminology +- Unique entity types +- Language-specific nuances + +### 1.4 The Need for Custom, Fine-tuned Models + +To address these challenges, fine-tuning custom NER models becomes essential. This approach allows for: +- Adaptation to specific domains: A fine-tuned model can perform better on specific tasks or domains compared to general-purpose model. +- Efficiency: Fine-tuned models often require less data and computational resources to achieve good performance on specific tasks. +- Faster inference: Smaller, task-specific models run faster than larger, general purpose ones. + +### 1.5 Project Objectives and Overview + +In this project, we aim to fine-tune a custom NER model for USPTO Patents. Our objectives include: +- Use [Hugging Face Spaces](https://huggingface.co/spaces) to setup an instance of [Argilla](https://argilla.io/). +- Use [Argilla](https://argilla.io/) UI to annotate our dataset with custom labels. +- Use Hugging Face [AutoTrain](https://huggingface.co/autotrain) to create a more efficient model in terms of size and inference speed. +- Demonstrating the effectiveness of transfer learning in NER tasks. + + +## 2. Data Background + + + + + +US Patent texts are typically long, descriptive documents about inventions. The data used in this tutorial can be accessed through the [Kaggle USPTO Competition](https://www.kaggle.com/competitions/uspto-explainable-ai). Each patent contains several fields: +- Title +- Abstract +- Claims +- Description + +For this tutorial, we'll focus on the `claims` field. + +### 2.1 Problem Statement + + + Our goal is to fine-tune a model to classify tokens in the `claims` field of a given patent. + +### 2.2 Breaking Down the Problem + + +To achieve this goal, we need: + +1. High-quality data to fine-tune a pretrained token classification model +2. Infrastructure to execute the training + +## 3. Create High-Quality Data with Argilla + [Argilla](https://github.com/argilla-io/argilla/) is an excellent tool for creating high-quality datasets with a user-friendly interface for labeling. + + + +### 3.1 Setting Up Argilla on Hugging Face Spaces + +#### 1. Visit [Hugging Face Spaces deployment page](https://huggingface.co/new-space?template=argilla/argilla-template-space) + +#### 2. Create a new space: + - Provide a name + - Select `Docker` as Space SDK + - Choose `Argilla` as Docker Template + - Leave other fields empty for simplicity + - Click on `Create Space` + +#### 3. Restart the Space + +Now you have an Argilla instance running on Hugging Face Spaces. Click on the space you created to go to the login screen of Argilla UI. +Access the UI using the credentials: + +- Username: `admin` +- Password: `12345678` [default password] + +For more options and setting up the Argilla instance for production use-cases, please refer to [Configure Argilla on Huggingface](https://docs.argilla.io/dev/getting_started/how-to-configure-argilla-on-huggingface/) + +### 3.2 Create a Dataset with Argilla Python SDK + +#### Step 1: Install & Import packages + + +```python +!pip install -U datasets argilla autotrain-advanced==0.8.8 > install_logs.txt 2>&1 +``` + + +```python +import argilla as rg +import pandas as pd +import re +import os +import random +import torch +from IPython.display import Image, display,HTML +from datasets import load_dataset, Dataset, DatasetDict,ClassLabel,Sequence,Value,Features +from transformers import pipeline,TokenClassificationPipeline +from typing import List, Dict, Union,Tuple +from google.colab import userdata +``` + +#### Step 2: Initialize the Argilla Client +api_url: We can get this URL by using the `https://huggingface.co/spaces/
+ | publication_number | +sequence_id | +tokens | +
---|---|---|---|
0 | +US-4444749-A | +0 | +A shampoo comprising an aqueous solution of an... | +
1 | +US-4444749-A | +1 | +A shampoo comprising an aqueous solution of an... | +
DatasetRecords: The provided batch size 256 was normalized. Using value 149. ++ + + + Sending records...: 100%|██████████| 1/1 [00:00<00:00, 1.71batch/s] + + + + + + DatasetRecords(Dataset(id=UUID('a187cdad-175e-4d87-989f-a529b9999bde') inserted_at=datetime.datetime(2024, 7, 28, 7, 23, 59, 902685) updated_at=datetime.datetime(2024, 7, 28, 7, 24, 1, 901701) name='claim_tokens' status='ready' guidelines='Classify individual tokens according to the specified categories, ensuring that any overlapping or nested entities are accurately captured.' allow_extra_metadata=False workspace_id=UUID('fd4fc24c-fc1f-4ffe-af41-d569432d6b50') last_activity_at=datetime.datetime(2024, 7, 28, 7, 24, 1, 901701) url=None)) + + + +Once, we have records pushed to Argilla Dataset, the UI will render the records and the labels for the annotator to annotate the text. + +Check the screeshots below. + + +```python +display_image("/content/images/annotation_screen.png") +``` + + + +![png](token_classification_tutorial_files/token_classification_tutorial_34_0.png) + + + +#### Step 6 : Annotate tokens in every records with appropriate labels. +Login to the Argilla UI and start annotating. + +Argilla UI : `https://huggingface.co/spaces/
Sample Train Tokens:" +
+ f"{train_tokens[0]}
Sample Valid Tokens:" +
+ f"{validation_tokens[0]}
Sample Train tags:" +
+ f"{train_ner_tags[0]}
Sample Valid tags:" +
+ f"{validation_ner_tags[0]}
"))
+```
+
+
+
+
+
+
+
+
+Sample Train Tokens:['The', 'FINFET', 'of', 'claim', '11', ',', 'wherein', 'the', 'conformal', 'gate', 'dielectric', 'comprises', 'a', 'high-κ', 'gate', 'dielectric', 'selected', 'from', 'the', 'group', 'consisting', 'of:', 'hafnium', 'oxide', '(HfO', '2', '),', 'lanthanum', 'oxide', '(La', '2', 'O', '3', '),', 'and', 'combinations', 'thereof.']
Sample Valid Tokens:['The', 'method', 'of', 'claim', '2', ',', 'wherein', 'generating', 'the', 'one', 'or', 'more', 'possible', 'design', 'modification', 'solutions', 'based', 'at', 'least', 'in', 'part', 'on', 'the', 'set', 'of', 'attack', 'mitigation', 'rules', 'comprises', 'generating', 'the', 'one', 'or', 'more', 'possible', 'design', 'modification', 'solutions', 'by', 'inputting', 'the', 'set', 'of', 'attack', 'mitigation', 'rules', 'to', 'a', 'model', 'configured', 'to', 'perform', 'structural', 'and', 'functional', 'analysis', 'to', 'interpret', 'the', 'set', 'of', 'attack', 'mitigation', 'rules,', 'wherein', 'the', 'set', 'of', 'attack', 'mitigation', 'rules', 'comprises', 'one', 'or', 'more', 'rules', 'used', 'by', 'the', 'model', 'to', 'identify', 'the', 'key-gate', 'type', 'for', 'each', 'possible', 'design', 'modification', 'solution', 'of', 'the', 'one', 'or', 'more', 'possible', 'design', 'modification', 'solutions', 'and', 'one', 'or', 'more', 'rules', 'used', 'by', 'the', 'model', 'to', 'identify', 'the', 'location', 'where', 'to', 'insert', 'the', 'key-gate', 'type', 'for', 'each', 'possible', 'design', 'modification', 'solution', 'of', 'the', 'one', 'or', 'more', 'possible', 'design', 'modification', 'solutions.']
Sample Train tags:['O', 'B-Electrical Circuit', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Chemical Compound', 'I-Chemical Compound', 'O', 'O', 'O', 'B-Chemical Compound', 'I-Chemical Compound', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
Sample Valid tags:['O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Process', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Process', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Algorithm', 'I-Algorithm', 'I-Algorithm', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Biotechnology', 'I-Biotechnology', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-Process', 'I-Process', 'O', 'O']
+
+
+As we are trying to have our data creation and model training pipeline working, for simplicity , I have dealing with one record each for training and validation.
+
+#### Step 4: Map labels (tags) to integers
+
+
+```python
+def mapped_ner_tags(ner_tags: List[List[str]]) -> List[List[int]]:
+ """
+ Convert a list of NER tags to their corresponding integer IDs.
+ This function takes a list of lists containing string NER tags, creates a unique mapping
+ of these tags to integer IDs, and then converts all tags to their respective IDs.
+ Args:
+ ner_tags (List[List[str]]): A list of lists, where each inner list contains string NER tags.
+ Returns:
+ List[List[int]]: A list of lists, where each inner list contains integer IDs
+ corresponding to the input NER tags.
+ Example:
+ >>> ner_tags = [['O', 'B-PER', 'I-PER'], ['O', 'B-ORG']]
+ >>> mapped_ner_tags(ner_tags)
+ [[0, 1, 2], [0, 3]]
+ Note:
+ The mapping of tags to IDs is created based on the unique tags present in the input.
+ The order of ID assignment may vary between function calls if the input changes.
+ """
+ labels = list(set([item for sublist in ner_tags for item in sublist]))
+ id2label = {i: label for i, label in enumerate(labels)}
+ label2id = {label: id_ for id_, label in id2label.items()}
+ mapped_ner_tags = [[label2id[label] for label in ner_tag] for ner_tag in ner_tags]
+ return mapped_ner_tags
+```
+
+
+```python
+def get_labels(ner_tags: List[List[str]]) -> List[str]:
+ """
+ Extract unique labels from a list of NER tag sequences.
+ This function takes a list of lists containing NER tags and returns a list of unique labels
+ found across all sequences.
+
+ Args:
+ ner_tags (List[List[str]]): A list of lists, where each inner list contains string NER tags.
+ Returns:
+ List[str]: A list of unique NER labels found in the input sequences.
+ Example:
+ >>> ner_tags = [['O', 'B-PER', 'I-PER'], ['O', 'B-ORG', 'I-ORG'], ['O', 'B-PER']]
+ >>> get_labels(ner_tags)
+ ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG']
+ Note:
+ The order of labels in the output list is not guaranteed to be consistent
+ between function calls, as it depends on the order of iteration over the set.
+ """
+ return list(set([item for sublist in ner_tags for item in sublist]))
+```
+
+#### Step 5: Argilla Dataset to HuggingFace Dataset
+We now have our data in a structure as required for token classification dataset. We will just have to create a Hugging Face Dataset.
+
+
+```python
+train_labels = get_labels(train_ner_tags)
+validation_labels = get_labels(validation_ner_tags)
+labels = list(set(train_labels + validation_labels))
+features = Features({
+ "tokens": Sequence(Value("string")),
+ "ner_tags": Sequence(ClassLabel(num_classes=len(labels), names=labels))
+})
+train_records = [
+ {
+ "tokens": token,
+ "ner_tags": ner_tag,
+ }
+ for token, ner_tag in zip(train_tokens, mapped_ner_tags(train_ner_tags))
+]
+validation_records = [
+ {
+ "tokens": token,
+ "ner_tags": ner_tag,
+ }
+ for token, ner_tag in zip(validation_tokens, mapped_ner_tags(validation_ner_tags))
+]
+span_dataset = DatasetDict(
+ {
+ "train": Dataset.from_list(train_records,features=features),
+ "validation": Dataset.from_list(validation_records,features=features),
+ }
+)
+
+```
+
+
+```python
+# assertion to verify if train split conforms the dataset structure required for fine-tuning.
+assert span_dataset['train'].features['ner_tags'].feature.names is not None
+```
+
+#### Step 6: Push dataset to Hugginface Hub
+
+
+```python
+!huggingface-cli login
+```
+
+
+ _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|
+ _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|
+ _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|
+ _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|
+ _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|
+
+ To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .
+ Enter your token (input will not be visible):
+ Add token as git credential? (Y/n) n
+ Token is valid (permission: write).
+ Your token has been saved to /root/.cache/huggingface/token
+ Login successful
+
+
+
+```python
+span_dataset.push_to_hub("bikashpatra/sample_claims_annotated_hf")
+```
+
+
+ Uploading the dataset shards: 0%| | 0/1 [00:00, ?it/s]
+
+
+
+ Creating parquet from Arrow format: 0%| | 0/1 [00:00, ?ba/s]
+
+
+
+ Uploading the dataset shards: 0%| | 0/1 [00:00, ?it/s]
+
+
+
+ Creating parquet from Arrow format: 0%| | 0/1 [00:00, ?ba/s]
+
+
+
+
+
+ CommitInfo(commit_url='https://huggingface.co/datasets/bikashpatra/sample_claims_annotated_hf/commit/e9faaa35dda423fcb2bccde9f19cbacd832af80a', commit_message='Upload dataset', commit_description='', oid='e9faaa35dda423fcb2bccde9f19cbacd832af80a', pr_url=None, pr_revision=None, pr_num=None)
+
+
+
+## 5. Model Fine-tuning using AutoTrain
+Huggingface [AutoTrain](https://huggingface.co/autotrain) is a simple tool to train model without writing a any code. We can use autotrain to fine-tune for a range of tasks like token-classification, text-generation, Image Classification and many more. In order to use AutoTrain, we will have to first create an instance of AutoTrain in HF space. Use the [create space](https://huggingface.co/new-space?template=autotrain-projects%2Fautotrain-advanced) link. For space SDK choose Docker and select AutoTrain as Docker template. We need to choose a hardware to train our model. Check the screenshots for a quick reference
+
+
+
+```python
+display_image("/content/images/autotrain_screen1.png")
+display_image("/content/images/autotrain_screen2.png")
+```
+
+
+
+![png](token_classification_tutorial_files/token_classification_tutorial_59_0.png)
+
+
+
+
+
+![png](token_classification_tutorial_files/token_classification_tutorial_59_1.png)
+
+
+
+### 5.1 Using AutoTrain UI
+
+After space creation, AutoTrain UI will allow us to select from range of tasks. We will have to configure our trainer on the AutoTrain UI.
+1. We will select Token classification as our task.
+2. For our tutorial we will fine-tune `google-bert/bert-base-uncased`. We can choose any model from the list.
+3. For DataSource select `Hugging Face Hub` which will give us a text box to fill in the dataset which we want to use for fine-tuning. We will use the dataset we pushed to Huggingface hub. I will be using the dataset that I pushed to huggingface hub `bikashpatra/claims_annotated_hf`
+4. Enter the keys for `train` and `validation` split.
+5. Under Column Mapping , enter the columns which store the tokens and tags. In my dataset , tokens are stored in `tokens` column and labels are stored in `ner_tags` column.
+With the above 5 inputs, we can trigger `Start Training` and AutoTrain will take care of fine-tuning the base model on our dataset.
+
+
+```python
+display_image("/content/images/autotrain_ui.png")
+```
+
+
+
+![png](token_classification_tutorial_files/token_classification_tutorial_62_0.png)
+
+
+
+### 5.2 Using AutoTrain CLI
+
+
+```python
+# for this cell to work, you will have to store HF_TOKEN as secret in colab notebook.
+os.environ['TOKEN'] = userdata.get('HF_TOKEN')
+```
+
+
+```python
+!autotrain token-classification --train \
+ --username "bikashpatra" \
+ --token $TOKEN \
+ --backend "spaces-a10g-small" \
+ --project-name "claims-token-classification" \
+ --data-path "bikashpatra/sample_claims_annotated_hf" \
+ --train-split "train" \
+ --valid-split "validation" \
+ --tokens-column "tokens" \
+ --tags-column "ner_tags" \
+ --model "distilbert-base-uncased" \
+ --lr "2e-5" \
+ --log "tensorboard" \
+ --epochs "10" \
+ --weight-decay "0.01" \
+ --warmup-ratio "0.1" \
+ --max-seq-length "256" \
+ --mixed-precision "fp16" \
+ --push-to-hub
+```
+
+ [1mINFO [0m | [32m2024-08-20 06:44:18[0m | [36mautotrain.cli.run_token_classification[0m:[36mrun[0m:[36m179[0m - [1mRunning Token Classification[0m
+ [33m[1mWARNING [0m | [32m2024-08-20 06:44:18[0m | [36mautotrain.trainers.common[0m:[36m__init__[0m:[36m180[0m - [33m[1mParameters supplied but not used: version, inference, config, func, train, deploy, backend[0m
+ [1mINFO [0m | [32m2024-08-20 06:44:22[0m | [36mautotrain.cli.run_token_classification[0m:[36mrun[0m:[36m185[0m - [1mJob ID: bikashpatra/autotrain-claims-token-classification[0m
+
+
+AutoTrain automatically creates huggingface space for us and triggers the training job. Link to the space created is `https://huggingface.co/spaces/$JOBID where JOBID is the value that we get from the logs of autotrain cli command.
+
+
+If the model training executes without any errors, our model is available with the value we provided to `--project-name`. In the above example it was `claims-token-classification`
+
+## 6. Inference
+With all the hardwork done, we have our model trained our custom dataset.We can use our trained model to predict labels for un-annotated rows.
+We will use [HF Pipelines](https://huggingface.co/docs/transformers/main_classes/pipelines) api. Pipelines are easy to use abstraction to load model and execute inference on un-seen data.In context of this tutorial _inference on un-seen text_ means predicting labels for tokens in un-annotated text.
+
+
+```python
+# Classify a sample text
+claims_text = """
+The FINFET of claim 11 , wherein the conformal gate dielectric comprises a high-κ gate dielectric selected from
+the group consisting of: hafnium oxide (HfO 2 ), lanthanum oxide (La 2 O 3 ), and combinations thereof.
+"""
+classifier = pipeline("token-classification", model="bikashpatra/claims-token-classification",device="cpu")
+preds = classifier(claims_text)
+```
+
+
+```python
+# The labels used for fine-tuning the model.
+classifier.model.config.id2label
+```
+
+
+
+
+ {0: 'B-Chemical Compound',
+ 1: 'I-Biotechnology',
+ 2: 'B-Electrical Circuit',
+ 3: 'B-Process',
+ 4: 'B-Biotechnology',
+ 5: 'O',
+ 6: 'I-Chemical Compound',
+ 7: 'I-Process',
+ 8: 'B-Algorithm',
+ 9: 'I-Algorithm'}
+
+
+
+## 7. Push predictions to Argilla Dataset
+Using [`rg.Query`](https://docs.argilla.io/latest/how_to_guides/query/) api we filter un-annotated data and predict tokens.
+
+The filter `rg.Filter(("response.status","==","pending"))` allows us to create a Argilla filter which we pass to [`rg.Query`](https://docs.argilla.io/latest/how_to_guides/query/) to get us all the records in Argilla dataset which has not been annotated.
+
+
+```python
+# Create a filter query to get only `pending` records in argilla dataset.
+status_filter = rg.Query(filter=rg.Filter(("response.status", "==", "pending")))
+
+submitted = rg_dataset.records(status_filter).to_list(flatten=True)
+claims = random.sample(submitted,k=10) # pick 10 random samples.
+
+spans = classifier(claims[0]['tokens'])
+```
+
+### 7.1 Helper function to predict the spans
+
+
+```python
+def predict_spanmarker(pipe:TokenClassificationPipeline,text: str):
+ """
+ Predict span markers for the given text using the provided pipeline.
+ Args:
+ pipe (TokenClassificationPipeline): A pipeline object for token classification.
+ text (str): The input text for which span markers are to be predicted.
+ Returns:
+ List[Dict[str, Union[int, str]]]: A list of dictionaries containing the predicted span markers.
+ Each dictionary should have 'start', 'end', and 'label' keys.
+ """
+ markers = pipe(text)
+ spans = [
+ {"label": marker["entity"][2:], "start": marker["start"], "end": marker["end"]}
+ for marker in markers if marker["entity"] != "O"
+ ]
+ return spans
+```
+
+
+```python
+updated_data=[
+ {
+ "span_label": predict_spanmarker(pipe=classifier, text=sample['tokens']),
+ "id": sample["id"],
+ }
+ for sample in claims
+]
+```
+
+
+```python
+# print a few predictions
+updated_data[0]['span_label'][:2]
+```
+
+
+
+
+ [{'label': 'Chemical Compound', 'start': 0, 'end': 3},
+ {'label': 'Process', 'start': 4, 'end': 10}]
+
+
+
+### 7.2 Insert records to Argilla Dataset.
+
+
+```python
+rg_dataset.records.log(records=updated_data)
+```
+
+
+DatasetRecords: The provided batch size 256 was normalized. Using value 10. ++ + + + Sending records...: 100%|██████████| 1/1 [00:00<00:00, 1.15batch/s] + + + + + + DatasetRecords(Dataset(id=UUID('a187cdad-175e-4d87-989f-a529b9999bde') inserted_at=datetime.datetime(2024, 7, 28, 7, 23, 59, 902685) updated_at=datetime.datetime(2024, 7, 28, 7, 35, 55, 80617) name='claim_tokens' status='ready' guidelines='Classify individual tokens according to the specified categories, ensuring that any overlapping or nested entities are accurately captured.' allow_extra_metadata=False distribution=None workspace_id=UUID('fd4fc24c-fc1f-4ffe-af41-d569432d6b50') last_activity_at=datetime.datetime(2024, 7, 28, 7, 35, 55, 80181))) + + + +The records we update here are stored as [`suggestions`](https://docs.argilla.io/latest/reference/argilla/records/suggestions/) and not [`responses`](https://docs.argilla.io/latest/reference/argilla/records/responses/). Responses in the context of this tutorial are created when annotator saves a annotation.Suggestions are labels predicted by model.Therefore, the records we updated here will have `response.status` as `pending` and not `submitted`. This will allow us/annotators to check the predicted labels and accept or reject model predictions. + +If we want to accept model predicted annotations for tokens in a text, we may save the [`suggestions`] as [`responses`], else we will have to add / remove / edit labels applied to tokens. + +## 8. Conclusion + +In this comprehensive tutorial, we've explored a complete workflow for data annotation and model fine-tuning. We began by setting up an [Argilla](https://argilla.io/) instance on [Hugging Face Spaces](https://huggingface.co/spaces), providing a robust platform for data management. We then configured and created a dataset within our Argilla instance, leveraging its user-friendly interface to manually annotate a subset of records. + +We continued as we exported the high-quality annotated data to a Hugging Face [dataset](https://huggingface.co/datasets), bridging the gap between annotation and model training. We then demonstrated the power of transfer learning by fine-tuning a `distilbert-base-uncased` model on this curated dataset using Hugging Face's [AutoTrain](https://huggingface.co/autotrain), a tool that simplifies the complexities of model training. + +The workflow came full circle as we applied our fine-tuned model to annotate the remaining unlabeled records in the Argilla dataset, showcasing how machine learning can accelerate the annotation process. This tutorial should provide a solid foundation for implementing an iterative annotation and fine-tuning pipeline while illustrating the synergy between human expertise and machine learning capabilities. + +This iterative approach allows for continuous improvement, making it an invaluable tool for tackling a wide range of natural language processing tasks efficiently and effectively. + +## 9. Acknowledgements + +I would like to express my sincere gratitude to the following individuals who have contributed to this notebook: + +- **[David Berenstein](https://x.com/davidberenstei)** for his invaluable insights and guidance. +- **[Sara Han](https://x.com/sdiazlor)** for answering my frequent queries on discord. + +This work would not have been possible without their support and expertise. + +Additionally, a nicer version of this notebook can be seen by replacing **github** in `https://github.com/bikash119/argilla/blob/argilla_with_autotrain/argilla/docs/community/token_classification_tutorial.ipynb` with **nbsanity**. Thanks to **[Hamel Hussain](https://x.com/HamelHusain)** for creating this notebook rendering utility. + + +```python + +``` diff --git a/argilla/docs/community/token_classification_tutorial.qmd b/argilla/docs/community/token_classification_tutorial.qmd new file mode 100644 index 0000000000..04ee5980b5 --- /dev/null +++ b/argilla/docs/community/token_classification_tutorial.qmd @@ -0,0 +1,654 @@ +--- +jupyter: python3 +--- + + + +# Fine-tuning a token classification model using custom Argilla Dataset and HuggingFace AutoTrain + +We all would want to try out to solve some use case with a neat tool / techs available out there. +In this tutorial , I want to go over my learning journey to fine tune a model on US Patent text. + +## 1. Introduction + + +### 1.1 Background on Named Entity Recognition (NER) + +Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that involves identifying and classifying named entities in text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, etc. + +### 1.2 Importance of NER in Natural Language Processing + +NER plays a crucial role in various NLP applications, including: +- Information Retrieval +- Question Answering Systems +- Machine Translation +- Text Summarization +- Sentiment Analysis + +### 1.3 Challenges in NER for Specific Domains or Languages + +While general-purpose NER models exist, they often fall short when applied to specialized domains or less-common languages due to: +- Domain-specific terminology +- Unique entity types +- Language-specific nuances + +### 1.4 The Need for Custom, Fine-tuned Models + +To address these challenges, fine-tuning custom NER models becomes essential. This approach allows for: +- Adaptation to specific domains: A fine-tuned model can perform better on specific tasks or domains compared to general-purpose model. +- Efficiency: Fine-tuned models often require less data and computational resources to achieve good performance on specific tasks. +- Faster inference: Smaller, task-specific models run faster than larger, general purpose ones. + +### 1.5 Project Objectives and Overview + +In this project, we aim to fine-tune a custom NER model for USPTO Patents. Our objectives include: +- Use [Hugging Face Spaces](https://huggingface.co/spaces) to setup an instance of [Argilla](https://argilla.io/). +- Use [Argilla](https://argilla.io/) UI to annotate our dataset with custom labels. +- Use Hugging Face [AutoTrain](https://huggingface.co/autotrain) to create a more efficient model in terms of size and inference speed. +- Demonstrating the effectiveness of transfer learning in NER tasks. + +## Data Background + + + + +US Patent texts are typically long, descriptive documents about inventions. The data used in this tutorial can be accessed through the [Kaggle USPTO Competition](https://www.kaggle.com/competitions/uspto-explainable-ai). Each patent contains several fields: +- Title +- Abstract +- Claims +- Description + +For this tutorial, we'll focus on the `claims` field. + +## Problem Statement + + Our goal is to fine-tune a model to classify tokens in the `claims` field of a given patent. + +## Breaking Down the Problem + +To achieve this goal, we need: + +1. High-quality data to fine-tune a pretrained token classification model +2. Infrastructure to execute the training + +## Create High-Quality Data with Argilla + [Argilla](https://github.com/argilla-io/argilla/) is an excellent tool for creating high-quality datasets with a user-friendly interface for labeling. + + +### Setting Up Argilla on Hugging Face Spaces + +#### 1. Visit [Hugging Face Spaces deployment page](https://huggingface.co/new-space?template=argilla/argilla-template-space) + +#### 2. Create a new space: + - Provide a name + - Select `Docker` as Space SDK + - Choose `Argilla` as Docker Template + - Leave other fields empty for simplicity + - Click on `Create Space` + +#### 3. Restart the Space + +Now you have an Argilla instance running on Hugging Face Spaces. Click on the space you created to go to the login screen of Argilla UI. +Access the UI using the credentials: + +- Username: `admin` +- Password: `12345678` [default password] + +For more options and setting up the Argilla instance for production use-cases, please refer to [Configure Argilla on Huggingface](https://docs.argilla.io/dev/getting_started/how-to-configure-argilla-on-huggingface/) + +### Create a Dataset with Argilla Python SDK + +#### Step 1: Install & Import packages + +```{python} +#| jupyter: {outputs_hidden: true} +!pip install -U datasets argilla autotrain-advanced==0.8.8 > install_logs.txt 2>&1 +``` + +```{python} +#| colab: {base_uri: 'https://localhost:8080/'} +!autotrain --version +``` + +```{python} +import argilla as rg +import pandas as pd +import re +import os +import random +import torch +from IPython.display import Image, display,HTML +from datasets import load_dataset, Dataset, DatasetDict,ClassLabel,Sequence,Value,Features +from transformers import pipeline,TokenClassificationPipeline +from typing import List, Dict, Union,Tuple +from google.colab import userdata +``` + +#### Step 2: Initialize the Argilla Client +api_url: We can get this URL by using the `https://huggingface.co/spaces/
Sample Train Tokens:" +
+ f"{train_tokens[0]}
Sample Valid Tokens:" +
+ f"{validation_tokens[0]}
Sample Train tags:" +
+ f"{train_ner_tags[0]}
Sample Valid tags:" +
+ f"{validation_ner_tags[0]}
"))
+```
+
+As we are trying to have our data creation and model training pipeline working, for simplicity , I have dealing with one record each for training and validation.
+
+#### Step 4: Map labels (tags) to integers
+
+```{python}
+def mapped_ner_tags(ner_tags: List[List[str]]) -> List[List[int]]:
+ """
+ Convert a list of NER tags to their corresponding integer IDs.
+ This function takes a list of lists containing string NER tags, creates a unique mapping
+ of these tags to integer IDs, and then converts all tags to their respective IDs.
+ Args:
+ ner_tags (List[List[str]]): A list of lists, where each inner list contains string NER tags.
+ Returns:
+ List[List[int]]: A list of lists, where each inner list contains integer IDs
+ corresponding to the input NER tags.
+ Example:
+ >>> ner_tags = [['O', 'B-PER', 'I-PER'], ['O', 'B-ORG']]
+ >>> mapped_ner_tags(ner_tags)
+ [[0, 1, 2], [0, 3]]
+ Note:
+ The mapping of tags to IDs is created based on the unique tags present in the input.
+ The order of ID assignment may vary between function calls if the input changes.
+ """
+ labels = list(set([item for sublist in ner_tags for item in sublist]))
+ id2label = {i: label for i, label in enumerate(labels)}
+ label2id = {label: id_ for id_, label in id2label.items()}
+ mapped_ner_tags = [[label2id[label] for label in ner_tag] for ner_tag in ner_tags]
+ return mapped_ner_tags
+```
+
+```{python}
+def get_labels(ner_tags: List[List[str]]) -> List[str]:
+ """
+ Extract unique labels from a list of NER tag sequences.
+ This function takes a list of lists containing NER tags and returns a list of unique labels
+ found across all sequences.
+
+ Args:
+ ner_tags (List[List[str]]): A list of lists, where each inner list contains string NER tags.
+ Returns:
+ List[str]: A list of unique NER labels found in the input sequences.
+ Example:
+ >>> ner_tags = [['O', 'B-PER', 'I-PER'], ['O', 'B-ORG', 'I-ORG'], ['O', 'B-PER']]
+ >>> get_labels(ner_tags)
+ ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG']
+ Note:
+ The order of labels in the output list is not guaranteed to be consistent
+ between function calls, as it depends on the order of iteration over the set.
+ """
+ return list(set([item for sublist in ner_tags for item in sublist]))
+```
+
+#### Step 5: Argilla Dataset to HuggingFace Dataset
+We now have our data in a structure as required for token classification dataset. We will just have to create a Hugging Face Dataset.
+
+```{python}
+train_labels = get_labels(train_ner_tags)
+validation_labels = get_labels(validation_ner_tags)
+labels = list(set(train_labels + validation_labels))
+features = Features({
+ "tokens": Sequence(Value("string")),
+ "ner_tags": Sequence(ClassLabel(num_classes=len(labels), names=labels))
+})
+train_records = [
+ {
+ "tokens": token,
+ "ner_tags": ner_tag,
+ }
+ for token, ner_tag in zip(train_tokens, mapped_ner_tags(train_ner_tags))
+]
+validation_records = [
+ {
+ "tokens": token,
+ "ner_tags": ner_tag,
+ }
+ for token, ner_tag in zip(validation_tokens, mapped_ner_tags(validation_ner_tags))
+]
+span_dataset = DatasetDict(
+ {
+ "train": Dataset.from_list(train_records,features=features),
+ "validation": Dataset.from_list(validation_records,features=features),
+ }
+)
+```
+
+```{python}
+# assertion to verify if train split conforms the dataset structure required for fine-tuning.
+assert span_dataset['train'].features['ner_tags'].feature.names is not None
+```
+
+#### Step 6: Push dataset to Hugginface Hub
+
+```{python}
+#| colab: {base_uri: 'https://localhost:8080/'}
+!huggingface-cli login
+```
+
+```{python}
+#| colab: {base_uri: 'https://localhost:8080/', height: 202, referenced_widgets: [e4f9cff7519b401a9db04f247b7e473c, 9b4e205349554a6fa38b50f19645181a, 9d85ae1441804dfba44528e1bc543a7d, 17c50ff23aa54b7ebb142c5df5615f78, f34a9eb89d804712bf6ee63dbc685017, 764dfd183d054eaa9ce458041be856ef, 9e7ada0da57c4f7a9994ae22e05407d5, d7aa2c0be22b41c2841047336bef2480, ebfe6ea4ec834638a0246cd40cda9ea4, d03837e77b97463687a54e7b81feb680, 70e15d43fccf47399525cbc6578618a2, 13636a22e24f4b1287bb4234f15f6a32, 3d6486dae69f4d50b471617044353d20, 2fb53aa82ac34a1fadac2a0a97840a15, 748b3a0616b544969b6ddaf8cd70092d, 945498057b214ee1a16de0391ebc87ab, 16343bcab2124dd28cfc5fccfb89f5a6, 2fdc2f2670824547a27ad9e298e10534, c47bc520c9684382a2c0e484a0f887ff, eafea0fa0b864434a723ec6391ef2876, 13e35558d8f0416fb99629ef3484bbab, 899f920fd1ea4ee2b230f89a337304ec, eec3edc793a84abeb1af22221b24b3c8, a7dfe44a19de4ffca626c2d610759297, c96f3210638442c195f0511b140793ba, c09bd90e783f4f5c88c2d57191fbdc19, 531daeb371ad4b51b1d493053d8b1e52, a1951348ae654b4583611c5e0425d81b, 34a5f08bf5f849c4829f3b83fd27bbec, 4a7301446bba44b798a70a27260e0d56, ccd15d0d08c743b08ac1419d25199a42, 11b6ad94ce8d419fb55a3817997037af, 4617899577864785a7a85be21c36fda4, 7ec8f069212f455589812568d35f7cde, b1266aa9efbd400b877df36a942f4337, b4daac723071481da2f3d7a5e1517b5f, c641704c716a40fcb3d54720cb4ff998, 4a39f8caa9214894a2a9e32cf0087105, 5e6fea490d71483b9fabf0d5f84f1934, e02a23c6f06f42639b82c65ce72246c2, 71449aa1769643c8b57094d35bc8b35f, 1303f70db20f45a39195b9872c8482ab, b01ba70e26da4b83b096c1b23da688d4, 35b2a6e0f8c543c4995e72d1543c39aa]}
+span_dataset.push_to_hub("bikashpatra/sample_claims_annotated_hf")
+```
+
+## Model Fine-tuning using AutoTrain
+Huggingface [AutoTrain](https://huggingface.co/autotrain) is a simple tool to train model without writing a any code. We can use autotrain to fine-tune for a range of tasks like token-classification, text-generation, Image Classification and many more. In order to use AutoTrain, we will have to first create an instance of AutoTrain in HF space. Use the [create space](https://huggingface.co/new-space?template=autotrain-projects%2Fautotrain-advanced) link. For space SDK choose Docker and select AutoTrain as Docker template. We need to choose a hardware to train our model. Check the screenshots for a quick reference
+
+```{python}
+#| colab: {base_uri: 'https://localhost:8080/', height: 1000}
+display_image("/content/images/autotrain_screen1.png")
+display_image("/content/images/autotrain_screen2.png")
+```
+
+### Using AutoTrain UI
+
+After space creation, AutoTrain UI will allow us to select from range of tasks. We will have to configure our trainer on the AutoTrain UI.
+1. We will select Token classification as our task.
+2. For our tutorial we will fine-tune `google-bert/bert-base-uncased`. We can choose any model from the list.
+3. For DataSource select `Hugging Face Hub` which will give us a text box to fill in the dataset which we want to use for fine-tuning. We will use the dataset we pushed to Huggingface hub. I will be using the dataset that I pushed to huggingface hub `bikashpatra/claims_annotated_hf`
+4. Enter the keys for `train` and `validation` split.
+5. Under Column Mapping , enter the columns which store the tokens and tags. In my dataset , tokens are stored in `tokens` column and labels are stored in `ner_tags` column.
+With the above 5 inputs, we can trigger `Start Training` and AutoTrain will take care of fine-tuning the base model on our dataset.
+
+```{python}
+#| colab: {base_uri: 'https://localhost:8080/', height: 625}
+display_image("/content/images/autotrain_ui.png")
+```
+
+### Using AutoTrain CLI
+
+```{python}
+# for this cell to work, you will have to store HF_TOKEN as secret in colab notebook.
+os.environ['TOKEN'] = userdata.get('HF_TOKEN')
+```
+
+```{python}
+#| colab: {base_uri: 'https://localhost:8080/'}
+!autotrain token-classification --train \
+ --username "bikashpatra" \
+ --token $TOKEN \
+ --backend "spaces-a10g-small" \
+ --project-name "claims-token-classification" \
+ --data-path "bikashpatra/sample_claims_annotated_hf" \
+ --train-split "train" \
+ --valid-split "validation" \
+ --tokens-column "tokens" \
+ --tags-column "ner_tags" \
+ --model "distilbert-base-uncased" \
+ --lr "2e-5" \
+ --log "tensorboard" \
+ --epochs "10" \
+ --weight-decay "0.01" \
+ --warmup-ratio "0.1" \
+ --max-seq-length "256" \
+ --mixed-precision "fp16" \
+ --push-to-hub
+```
+
+AutoTrain automatically creates huggingface space for us and triggers the training job. Link to the space created is `https://huggingface.co/spaces/$JOBID where JOBID is the value that we get from the logs of autotrain cli command.
+
+If the model training executes without any errors, our model is available with the value we provided to `--project-name`. In the above example it was `claims-token-classification`
+
+## Inference
+With all the hardwork done, we have our model trained our custom dataset.We can use our trained model to predict labels for un-annotated rows.
+We will use [HF Pipelines](https://huggingface.co/docs/transformers/main_classes/pipelines) api. Pipelines are easy to use abstraction to load model and execute inference on un-seen data.In context of this tutorial _inference on un-seen text_ means predicting labels for tokens in un-annotated text.
+
+```{python}
+# Classify a sample text
+claims_text = """
+The FINFET of claim 11 , wherein the conformal gate dielectric comprises a high-κ gate dielectric selected from
+the group consisting of: hafnium oxide (HfO 2 ), lanthanum oxide (La 2 O 3 ), and combinations thereof.
+"""
+classifier = pipeline("token-classification", model="bikashpatra/claims-token-classification",device="cpu")
+preds = classifier(claims_text)
+```
+
+```{python}
+#| colab: {base_uri: 'https://localhost:8080/'}
+# The labels used for fine-tuning the model.
+classifier.model.config.id2label
+```
+
+## Push predictions to Argilla Dataset
+Using [`rg.Query`](https://docs.argilla.io/latest/how_to_guides/query/) api we filter un-annotated data and predict tokens.
+
+The filter `rg.Filter(("response.status","==","pending"))` allows us to create a Argilla filter which we pass to [`rg.Query`](https://docs.argilla.io/latest/how_to_guides/query/) to get us all the records in Argilla dataset which has not been annotated.
+
+```{python}
+# Create a filter query to get only `pending` records in argilla dataset.
+status_filter = rg.Query(filter=rg.Filter(("response.status", "==", "pending")))
+
+submitted = rg_dataset.records(status_filter).to_list(flatten=True)
+claims = random.sample(submitted,k=10) # pick 10 random samples.
+
+spans = classifier(claims[0]['tokens'])
+```
+
+### Helper function to predict the spans
+
+```{python}
+def predict_spanmarker(pipe:TokenClassificationPipeline,text: str):
+ """
+ Predict span markers for the given text using the provided pipeline.
+ Args:
+ pipe (TokenClassificationPipeline): A pipeline object for token classification.
+ text (str): The input text for which span markers are to be predicted.
+ Returns:
+ List[Dict[str, Union[int, str]]]: A list of dictionaries containing the predicted span markers.
+ Each dictionary should have 'start', 'end', and 'label' keys.
+ """
+ markers = pipe(text)
+ spans = [
+ {"label": marker["entity"][2:], "start": marker["start"], "end": marker["end"]}
+ for marker in markers if marker["entity"] != "O"
+ ]
+ return spans
+```
+
+```{python}
+updated_data=[
+ {
+ "span_label": predict_spanmarker(pipe=classifier, text=sample['tokens']),
+ "id": sample["id"],
+ }
+ for sample in claims
+]
+```
+
+```{python}
+#| colab: {base_uri: 'https://localhost:8080/'}
+# print a few predictions
+updated_data[0]['span_label'][:2]
+```
+
+### Insert records to Argilla Dataset.
+
+```{python}
+#| colab: {base_uri: 'https://localhost:8080/', height: 137}
+rg_dataset.records.log(records=updated_data)
+```
+
+The records we update here are stored as [`suggestions`](https://docs.argilla.io/latest/reference/argilla/records/suggestions/) and not [`responses`](https://docs.argilla.io/latest/reference/argilla/records/responses/). Responses in the context of this tutorial are created when annotator saves a annotation.Suggestions are labels predicted by model.Therefore, the records we updated here will have `response.status` as `pending` and not `submitted`. This will allow us/annotators to check the predicted labels and accept or reject model predictions.
+
+If we want to accept model predicted annotations for tokens in a text, we may save the [`suggestions`] as [`responses`], else we will have to add / remove / edit labels applied to tokens.
+
+## Conclusion
+
+In this comprehensive tutorial, we've explored a complete workflow for data annotation and model fine-tuning. We began by setting up an [Argilla](https://argilla.io/) instance on [Hugging Face Spaces](https://huggingface.co/spaces), providing a robust platform for data management. We then configured and created a dataset within our Argilla instance, leveraging its user-friendly interface to manually annotate a subset of records.
+
+We continued as we exported the high-quality annotated data to a Hugging Face [dataset](https://huggingface.co/datasets), bridging the gap between annotation and model training. We then demonstrated the power of transfer learning by fine-tuning a `distilbert-base-uncased` model on this curated dataset using Hugging Face's [AutoTrain](https://huggingface.co/autotrain), a tool that simplifies the complexities of model training.
+
+The workflow came full circle as we applied our fine-tuned model to annotate the remaining unlabeled records in the Argilla dataset, showcasing how machine learning can accelerate the annotation process. This tutorial should provide a solid foundation for implementing an iterative annotation and fine-tuning pipeline while illustrating the synergy between human expertise and machine learning capabilities.
+
+This iterative approach allows for continuous improvement, making it an invaluable tool for tackling a wide range of natural language processing tasks efficiently and effectively.
+
+
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