{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nydD1iOK5UT8", "outputId": "54769217-59da-4afd-b9ba-990325dafdac" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting datasets\n", " Downloading datasets-2.21.0-py3-none-any.whl.metadata (21 kB)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.4)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n", "Collecting pyarrow>=15.0.0 (from datasets)\n", " Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n", "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n", " Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n", "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.1.4)\n", "Requirement already satisfied: 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This behaviour is the source of the following dependency conflicts.\n", "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n", "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed datasets-2.21.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.5.0\n", "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.42.4)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n", "Requirement already satisfied: numpy<2.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from 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Attempting uninstall: pydantic\n", " Found existing installation: pydantic 2.8.2\n", " Uninstalling pydantic-2.8.2:\n", " Successfully uninstalled pydantic-2.8.2\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "sqlalchemy 2.0.32 requires typing-extensions>=4.6.0, but you have typing-extensions 4.5.0 which is incompatible.\n", "albucore 0.0.13 requires typing-extensions>=4.9.0, but you have typing-extensions 4.5.0 which is incompatible.\n", "albumentations 1.4.14 requires pydantic>=2.7.0, but you have pydantic 1.10.18 which is incompatible.\n", "albumentations 1.4.14 requires typing-extensions>=4.9.0, but you have typing-extensions 4.5.0 which is incompatible.\n", "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n", "fastapi 0.112.2 requires typing-extensions>=4.8.0, but you have typing-extensions 4.5.0 which is incompatible.\n", "gradio 4.42.0 requires pydantic>=2.0, but you have pydantic 1.10.18 which is incompatible.\n", "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\n", "pydantic-core 2.20.1 requires typing-extensions!=4.7.0,>=4.6.0, but you have typing-extensions 4.5.0 which is incompatible.\n", "torch 2.3.1+cu121 requires typing-extensions>=4.8.0, but you have typing-extensions 4.5.0 which is incompatible.\n", "typeguard 4.3.0 requires typing-extensions>=4.10.0, but you have typing-extensions 4.5.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed boto3-1.35.5 botocore-1.35.5 cohere-5.6.2 fastavro-1.9.5 httpx-sse-0.4.0 jmespath-1.0.1 kaleido-0.2.1 openai-1.5.0 parameterized-0.9.0 pydantic-1.10.18 s3transfer-0.10.2 tiktoken-0.7.0 types-requests-2.32.0.20240712 typing-extensions-4.5.0\n" ] } ], "source": [ "!pip install datasets\n", "!pip install transformers\n", "!pip install sentencepiece\n", "!pip install transformers[torch]`\n", "!pip install sacrebleu\n", "!pip install evaluate\n", "!pip install sacrebleu\n", "!pip install accelerate -U\n", "!pip install gradio\n", "!pip install kaleido cohere openai tiktoken typing-extensions==4.5.0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7V9Nq_Yd5V2x", "colab": { "base_uri": "https://localhost:8080/", "height": 397, "referenced_widgets": [ "8ac932778a4542b897de7fbdef904010", "4fa354e42c47425c8fc0af21d57e4be5", "adc0fd19f0654e8889a4da75db126f53", "59f5d9f4244b4fb88fe44a795c29d2b3", "3f956c7cd74f4da9b8d18b921c6d9c00", "05777f1546d94745acd1425b56898fba", "cc428a06c1cb473bacb2d74eea180367", "58bd3b4dcc834ef188a9822e00de6d24", "ef4a6c289a0240d19e08e93d4ee085d8", "ae767c1bcc944428a18425b43ade1ead", "42b36785d54c4f438f3cd9dd8a9665a7", "72907669de364d7b85c60cb224e76634", "4fa6a00537814108af64652ba581a46d", "dc4d4cd73c0c48b8990a5ecac4b1e907", "18038cfbb4664d82bc928a2a5beb08c3", "7bad2d26952243c185dda05291844000", "4faa60bb451c48ee9b9065be4e4e8a8b", "4597902756fc44d4bb7be600ce12b814", "ecf2a2b4cccb472988599d01dadc2959", "cf372b7e1af341b28358a5b1b079637f", "bef535ea7e6948e88a7d99c24720ed87", "74eb11a1566e4295811907c4ae1b0161", "2f72a9c1e0ce4a75b39982fc4e878e1c", 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"be0c80af80ca4e049167ba7174b8b4af", "4f3947e2c8c74e1abb1c030ff84034a1", "1612416544a245299af798e0b73a0613", "b670c0f2ad4c4c268a7ba54bb1b48c32", "ea84b4edf79141aaae9f366ad43a239c", "8eeef8f9f7934d6199a7743688be93c8", "3ef2d183d8074ea581328305d77d7799", "6153e09c912941f985a8f810a1c16bfd", "5f330f016e4b4eab822041579ed45854", "747e7ccb4b79434c94f1553be9abc2a7", "7fd1db959ad940ada4fe360ea6d0f6aa" ] }, "outputId": "44d02db7-ff1e-48f9-d25e-8f14f3418197" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/3.14k [00:00" ], "text/html": [ "\n", "
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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n", "Non-default generation parameters: {'max_length': 512, 'num_beams': 4, 'bad_words_ids': [[61949]], 'forced_eos_token_id': 0}\n", "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", " warnings.warn(\n", "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n", "Non-default generation parameters: {'max_length': 512, 'num_beams': 4, 'bad_words_ids': [[61949]], 'forced_eos_token_id': 0}\n", "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", " warnings.warn(\n", "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n", "Non-default generation parameters: {'max_length': 512, 'num_beams': 4, 'bad_words_ids': [[61949]], 'forced_eos_token_id': 0}\n", "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", " warnings.warn(\n", "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n", "Non-default generation parameters: {'max_length': 512, 'num_beams': 4, 'bad_words_ids': [[61949]], 'forced_eos_token_id': 0}\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=2000, training_loss=2.6001077880859373, metrics={'train_runtime': 983.9536, 'train_samples_per_second': 65.044, 'train_steps_per_second': 2.033, 'total_flos': 1107133575266304.0, 'train_loss': 2.6001077880859373, 'epoch': 25.31645569620253})" ] }, "metadata": {}, "execution_count": 13 } ], "source": [ "from transformers import Seq2SeqTrainer\n", "\n", "trainer = Seq2SeqTrainer(\n", " model,\n", " training_args,\n", " train_dataset=tokenized_datasets_test,\n", " eval_dataset=tokenized_datasets_validation,\n", " data_collator=data_collator,\n", " tokenizer=tokenizer,\n", " compute_metrics=compute_metrics,\n", ")\n", "\n", "trainer.train()\n" ] } ], "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, 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