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/usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n", "Installing collected packages: dill, multiprocess, datasets\n", "Successfully installed datasets-2.18.0 dill-0.3.8 multiprocess-0.70.16\n" ] } ], "source": [ "# Transformers installation\n", "! pip install transformers datasets\n", "# To install from source instead of the last release, comment the command above and uncomment the following one.\n", "# ! pip install git+https://github.com/huggingface/transformers.git" ] }, { "cell_type": "markdown", "metadata": { "id": "kRRjAH_ZAwxi" }, "source": [ "# Text classification" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "hide_input": true, "id": "mJcVlLqoAwxj", "outputId": "0110dcb9-d1b2-4c73-dc83-a4d38de20c51" }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#@title\n", "from IPython.display import HTML\n", "\n", "HTML('')" ] }, { "cell_type": "markdown", "metadata": { "id": "xi5wf8QxAwxk" }, "source": [ "Text classification is a common NLP task that assigns a label or class to text. Some of the largest companies run text classification in production for a wide range of practical applications. One of the most popular forms of text classification is sentiment analysis, which assigns a label like 🙂 positive, 🙁 negative, or 😐 neutral to a sequence of text.\n", "\n", "This guide will show you how to:\n", "\n", "1. Finetune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [IMDb](https://huggingface.co/datasets/imdb) dataset to determine whether a movie review is positive or negative.\n", "2. Use your finetuned model for inference.\n", "\n", "\n", "The task illustrated in this tutorial is supported by the following model architectures:\n", "\n", "\n", "\n", "[ALBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/albert), [BART](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/bart), [BERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/bert), [BigBird](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/big_bird), [BigBird-Pegasus](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/bigbird_pegasus), [BioGpt](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/biogpt), [BLOOM](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/bloom), [CamemBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/camembert), [CANINE](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/canine), [ConvBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/convbert), [CTRL](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/ctrl), [Data2VecText](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/data2vec-text), [DeBERTa](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/deberta), [DeBERTa-v2](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/deberta-v2), [DistilBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/distilbert), [ELECTRA](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/electra), [ERNIE](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/ernie), [ErnieM](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/ernie_m), [ESM](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/esm), [FlauBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/flaubert), [FNet](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/fnet), [Funnel Transformer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/funnel), [GPT-Sw3](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/gpt-sw3), [OpenAI GPT-2](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/gpt2), [GPTBigCode](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/gpt_bigcode), [GPT Neo](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/gpt_neo), [GPT NeoX](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/gpt_neox), [GPT-J](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/gptj), [I-BERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/ibert), [LayoutLM](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/layoutlm), [LayoutLMv2](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/layoutlmv2), [LayoutLMv3](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/layoutlmv3), [LED](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/led), [LiLT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/lilt), [LLaMA](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/llama), [Longformer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/longformer), [LUKE](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/luke), [MarkupLM](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/markuplm), [mBART](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mbart), [MEGA](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mega), [Megatron-BERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/megatron-bert), [MobileBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mobilebert), [MPNet](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mpnet), [MVP](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mvp), [Nezha](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/nezha), [Nyströmformer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/nystromformer), [OpenLlama](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/open-llama), [OpenAI GPT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/openai-gpt), [OPT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/opt), [Perceiver](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/perceiver), [PLBart](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/plbart), [QDQBert](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/qdqbert), [Reformer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/reformer), [RemBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/rembert), [RoBERTa](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/roberta), [RoBERTa-PreLayerNorm](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/roberta-prelayernorm), [RoCBert](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/roc_bert), [RoFormer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/roformer), [SqueezeBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/squeezebert), [TAPAS](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/tapas), [Transformer-XL](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/transfo-xl), [XLM](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/xlm), [XLM-RoBERTa](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/xlm-roberta), [XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/xlm-roberta-xl), [XLNet](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/xlnet), [X-MOD](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/xmod), [YOSO](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/yoso)\n", "\n", "\n", "\n", "\n", "\n", "\n", "Before you begin, make sure you have all the necessary libraries installed:\n", "\n", "```bash\n", "pip install transformers datasets evaluate\n", "```\n", "\n", "We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "0zrBI8sBAwxl", "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "63e5165d2ae643ea8def3b4141a39c4a", "ac63993121d3452689b3575bffa5082a", "256f6c6ba82f4a5bb9256b6480ac3055", "f70cf9a1d5c740a78ebb8c2300564d87", "f01beb3384c346d2ad62fd8d07f05b97", "2f90cafa30c045ac97688be9b24e0db7", "b276281712414480b6f9584d73c63371", "691c60bed4944d11a6d3bd8753d7c963", "1b95093f83834f54b7cc082d7fdfd0bf", "0c83aecf91594b0f8485fe6d99356cb2", "b311a289c96446e695b206df6024d5b4", "931f58fc435a4fd9a1b57a1fa8ab6914", "52e8fb18a744452fb21fa0db0efc4ffa", "cd1e05bff56e4f859f1b82d26df8b3b9", "590ffb14480d4152afec3792ed33008e", "a9e5fdc599a743d48258d36ec893964a", "34e776da3a0043bdaa319ed059a90801", "33e7d9d09c2845df81bca8eccfe20e10", "0e88fe140ec5438fbc1000c6d296c4bb", "e2c339d675084939a7ce9ba4e3fa7517", "0e01d5b07374418c95d4166a22a03bcc", "26e8fc4586df459b8f21a42b5ffd78ae", "71857d17de8a4f7b93edb40f791c7797", "35073580241f4f60aa9c9276a69b5f3f", "80e2b384bece41c995bbf4b94297ad6d", "cedcf1ff9bc7485087e0e11d1d132773", "9184ed5b9acf4b248ac7588e87a71932", "c789bb474a7547b08c643aa76e23b447", "b50a077398cb41cf9d50158632135d6a", "2863107c10564057bb40a24abb33f445", "dd9f1d7dfbec4312a13e19c3ac16057a", "77cb887e9a924fafb4c54eed59c91ebb" ] }, "outputId": "fdc47bf8-f33f-4d43-98dc-bdfc3160e860" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
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python-dateutil>=2.8.1->pandas->evaluate) (1.16.0)\n", "Installing collected packages: responses, evaluate\n", "Successfully installed evaluate-0.4.1 responses-0.18.0\n" ] } ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "OlWJIwc6Awxo", "outputId": "49ad1f79-9307-4528-94d7-c851c00a2f80", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "afa70294ca064b6b8d0f47330c0a2d72", "2fbceac30a814924a0006dd4b2bdbba9", "94506e65c86e4654ae9ac0f640e99ce3", "d9456bcd556c4d45ae53aabde62b9732", "ee6756bb68914c0098f92c2fa2c152d3", "675d58dffdb9455d9a6bfe337ec07283", "744ca6a09e5f4b918e51843893c58c60", "f6bff9e14c4b4423b6e0eb5f38d11bee", "3f1889664b73405c891cf0c8ed2df3d7", "6662c16f91034dbf92a8da01f37ab063", "a1bb0cdbf6ff48d484ec432b5d1738f5" ] } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/4.20k [00:00\n", "\n", "If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial [here](https://huggingface.co/docs/transformers/main/en/tasks/../training#train-a-tensorflow-model-with-keras)!\n", "\n", "\n", "To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "nGQyRjTuAwxo" }, "outputs": [], "source": [ "from transformers import create_optimizer\n", "import tensorflow as tf\n", "\n", "batch_size = 16\n", "num_epochs = 5\n", "batches_per_epoch = len(tokenized_data[\"train\"]) // batch_size\n", "total_train_steps = int(batches_per_epoch * num_epochs)\n", "optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)" ] }, { "cell_type": "markdown", "metadata": { "id": "EjT2wNgaAwxo" }, "source": [ "Then you can load DistilBERT with [TFAutoModelForSequenceClassification](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.TFAutoModelForSequenceClassification) along with the number of expected labels, and the label mappings:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "Czp_lnZRAwxp", "outputId": "590aa84a-abfd-4c23-e401-a0ed49150d11", "colab": { "base_uri": "https://localhost:8080/", "height": 138, "referenced_widgets": [ "a989fc391efe4517bcbf4fe67e22151e", "6f243925d628405b9747b27ba28d3945", "34a0a0a97c7141a1bc11e641c04710ee", "af4027b7c4e04219b6096758d5cef6b2", "8c42ee14fabb4312ac286a52bcd34b57", "01b2e48b495f45d18e0057496f4897fc", "a8bbd79368064480bd95272323fbf3cb", "94106e8c53c44606972c0e75756afe5a", "4ce13ce6dcb44626a4ae2e3b744baef9", "6ebc58b0a6a54b2e8a2b37b709f80e53", "f97566eb87ae493685c3a2a1a43bfce8" ] } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/331M [00:00" ] }, "metadata": {}, "execution_count": 23 } ], "source": [ "model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)" ] }, { "cell_type": "markdown", "metadata": { "id": "9fqIYMvwAwxt" }, "source": [ "Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!\n", "\n", "\n", "\n", "For a more in-depth example of how to finetune a model for text classification, take a look at the corresponding\n", "[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb)\n", "or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb).\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "X1VBMsD3Awxu" }, "source": [ "## Inference" ] }, { "cell_type": "markdown", "metadata": { "id": "Q1tsnVT4Awxu" }, "source": [ "Great, now that you've finetuned a model, you can use it for inference!\n", "\n", "Grab some text you'd like to run inference on:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "2wadXJm5Awxu" }, "outputs": [], "source": [ "text = \"This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three.\"" ] }, { "cell_type": "markdown", "metadata": { "id": "Kfv3frDSAwxu" }, "source": [ "The simplest way to try out your finetuned model for inference is to use it in a [pipeline()](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines#transformers.pipeline). Instantiate a `pipeline` for sentiment analysis with your model, and pass your text to it:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "cmV1hPffAwxu", "outputId": "7e0ea78e-a2ca-4719-88ab-2ef8a01f4b9f", "colab": { "base_uri": "https://localhost:8080/", "height": 547 } }, "outputs": [ { "output_type": "error", "ename": "OSError", "evalue": "reem442/emo does not appear to have a file named config.json. Checkout 'https://huggingface.co/reem442/emo/main' for available files.", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mHTTPError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 287\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1020\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mHTTPError\u001b[0m: 404 Client Error: Not Found for url: https://huggingface.co/reem442/emo/resolve/main/config.json", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mEntryNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;31m# Load from URL or cache if already cached\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m resolved_file = hf_hub_download(\n\u001b[0m\u001b[1;32m 399\u001b[0m \u001b[0mpath_or_repo_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout, endpoint)\u001b[0m\n\u001b[1;32m 1237\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1238\u001b[0;31m metadata = get_hf_file_metadata(\n\u001b[0m\u001b[1;32m 1239\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36mget_hf_file_metadata\u001b[0;34m(url, token, proxies, timeout, library_name, library_version, user_agent)\u001b[0m\n\u001b[1;32m 1630\u001b[0m \u001b[0;31m# Retrieve metadata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1631\u001b[0;31m r = _request_wrapper(\n\u001b[0m\u001b[1;32m 1632\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"HEAD\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfollow_relative_redirects\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m response = _request_wrapper(\n\u001b[0m\u001b[1;32m 386\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m 408\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m \u001b[0mhf_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 410\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"{response.status_code} Client Error.\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"\\n\\n\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34mf\"Entry Not Found for url: {response.url}.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mEntryNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mEntryNotFoundError\u001b[0m: 404 Client Error. (Request ID: Root=1-65eca511-6d5ac2aa41436e6476b129cf;f55c45bc-cab3-4874-a0a5-8b1197525f5a)\n\nEntry Not Found for url: https://huggingface.co/reem442/emo/resolve/main/config.json.", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mclassifier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sentiment-analysis\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"reem442/emo\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter_config\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"base_model_name_or_path\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 814\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 815\u001b[0;31m config = AutoConfig.from_pretrained(\n\u001b[0m\u001b[1;32m 816\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_from_pipeline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode_revision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcode_revision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhub_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 817\u001b[0m )\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 1109\u001b[0m \u001b[0mcode_revision\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 632\u001b[0m \u001b[0;31m# Get config dict associated with the base config file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 633\u001b[0;31m \u001b[0mconfig_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_config_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 634\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"_commit_hash\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconfig_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[0moriginal_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"_commit_hash\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"_commit_hash\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py\u001b[0m in \u001b[0;36m_get_config_dict\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 686\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 687\u001b[0m \u001b[0;31m# Load from local folder or from cache or download from model Hub and cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m resolved_config_file = cached_file(\n\u001b[0m\u001b[1;32m 689\u001b[0m \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 690\u001b[0m \u001b[0mconfiguration_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrevision\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 451\u001b[0m \u001b[0mrevision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"main\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m raise EnvironmentError(\n\u001b[0m\u001b[1;32m 453\u001b[0m \u001b[0;34mf\"{path_or_repo_id} does not appear to have a file named {full_filename}. Checkout \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0;34mf\"'https://huggingface.co/{path_or_repo_id}/{revision}' for available files.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mOSError\u001b[0m: reem442/emo does not appear to have a file named config.json. Checkout 'https://huggingface.co/reem442/emo/main' for available files." ] } ], "source": [ "from transformers import pipeline\n", "\n", "classifier = pipeline(\"sentiment-analysis\", model=\"stevhliu/my_awesome_model\")\n", "classifier(text)" ] }, { "cell_type": "markdown", "metadata": { "id": "pUIAd73mAwxu" }, "source": [ "You can also manually replicate the results of the `pipeline` if you'd like:\n", "\n", "Tokenize the text and return TensorFlow tensors:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MnjMuYlLAwxu" }, "outputs": [], "source": [ "from transformers import AutoTokenizer\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"stevhliu/my_awesome_model\")\n", "inputs = tokenizer(text, return_tensors=\"tf\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ghPFR3W3Awxu" }, "source": [ "Pass your inputs to the model and return the `logits`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2ls931AvAwxu" }, "outputs": [], "source": [ "from transformers import TFAutoModelForSequenceClassification\n", "\n", "model = TFAutoModelForSequenceClassification.from_pretrained(\"stevhliu/my_awesome_model\")\n", "logits = model(**inputs).logits" ] }, { "cell_type": "markdown", "metadata": { "id": "ne4I6WwwAwxu" }, "source": [ "Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a text label:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aXi3dcUPAwxu", "outputId": "4ea8345e-103a-4958-edd0-3886c50950ee" }, "outputs": [ { "data": { "text/plain": [ "'POSITIVE'" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])\n", "model.config.id2label[predicted_class_id]" ] } ], "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "language_info": { "name": "python" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "63e5165d2ae643ea8def3b4141a39c4a": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [ "IPY_MODEL_0e01d5b07374418c95d4166a22a03bcc", "IPY_MODEL_26e8fc4586df459b8f21a42b5ffd78ae", "IPY_MODEL_71857d17de8a4f7b93edb40f791c7797", "IPY_MODEL_35073580241f4f60aa9c9276a69b5f3f" ], "layout": "IPY_MODEL_b276281712414480b6f9584d73c63371" } }, "ac63993121d3452689b3575bffa5082a": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_691c60bed4944d11a6d3bd8753d7c963", "placeholder": "​", "style": "IPY_MODEL_1b95093f83834f54b7cc082d7fdfd0bf", "value": "

Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
" } }, "256f6c6ba82f4a5bb9256b6480ac3055": { "model_module": "@jupyter-widgets/controls", "model_name": "PasswordModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "PasswordModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "PasswordView", "continuous_update": true, "description": "Token:", "description_tooltip": null, "disabled": false, "layout": "IPY_MODEL_0c83aecf91594b0f8485fe6d99356cb2", "placeholder": "​", "style": "IPY_MODEL_b311a289c96446e695b206df6024d5b4", "value": "" } }, "f70cf9a1d5c740a78ebb8c2300564d87": { "model_module": "@jupyter-widgets/controls", "model_name": "CheckboxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "CheckboxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "CheckboxView", "description": "Add token as git credential?", "description_tooltip": null, "disabled": false, "indent": true, "layout": "IPY_MODEL_931f58fc435a4fd9a1b57a1fa8ab6914", "style": "IPY_MODEL_52e8fb18a744452fb21fa0db0efc4ffa", "value": true } }, "f01beb3384c346d2ad62fd8d07f05b97": { "model_module": "@jupyter-widgets/controls", "model_name": "ButtonModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ButtonModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ButtonView", "button_style": "", "description": "Login", "disabled": false, "icon": "", "layout": "IPY_MODEL_cd1e05bff56e4f859f1b82d26df8b3b9", "style": "IPY_MODEL_590ffb14480d4152afec3792ed33008e", "tooltip": "" } }, "2f90cafa30c045ac97688be9b24e0db7": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_a9e5fdc599a743d48258d36ec893964a", "placeholder": "​", "style": "IPY_MODEL_34e776da3a0043bdaa319ed059a90801", "value": "\nPro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks.
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