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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# osiria/deberta-italian-question-answering\n",
"## test set metrics"
],
"metadata": {
"id": "oub2ir8ZDkrH"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "02K9xO6Gvto1",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0d492963-a525-4d5a-ac41-7cf1a51008a8"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting git+https://github.com/huggingface/transformers\n",
" Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-dhlxsg5a\n",
" Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers /tmp/pip-req-build-dhlxsg5a\n",
" Resolved https://github.com/huggingface/transformers to commit df5c5c62ae253055336f5bb0828ca8e3e15ab6bd\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.0.dev0) (3.13.1)\n",
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]
}
],
"source": [
"!pip install git+https://github.com/huggingface/transformers"
]
},
{
"cell_type": "code",
"source": [
"!pip install datasets"
],
"metadata": {
"id": "VKgybrUswa1q",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3a52c215-7046-4185-cb95-884032b34007"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
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]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install accelerate"
],
"metadata": {
"id": "xYEW0Hjqz2lo",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "391d2364-0eb8-4fb0-9ccb-dcc6b1a3d3a2"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
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]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install evaluate"
],
"metadata": {
"id": "9OFz3JdR0cQ_",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c8c102c7-29ec-4551-8994-324853530b56"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
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]
}
]
},
{
"cell_type": "code",
"source": [
"!wget https://raw.githubusercontent.com/huggingface/transformers/main/examples/pytorch/question-answering/run_qa.py\n",
"!wget https://raw.githubusercontent.com/huggingface/transformers/main/examples/pytorch/question-answering/trainer_qa.py\n",
"!wget https://raw.githubusercontent.com/huggingface/transformers/main/examples/pytorch/question-answering/utils_qa.py"
],
"metadata": {
"id": "JJhpBNwpxOTK",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7c34e19d-a391-41a2-8cd5-8abfe3788c6e"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2023-12-09 13:46:17-- https://raw.githubusercontent.com/huggingface/transformers/main/examples/pytorch/question-answering/run_qa.py\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.110.133, ...\n",
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"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
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]
},
{
"cell_type": "code",
"source": [
"!ls"
],
"metadata": {
"id": "Loa6y9XqzXJ4",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "657803ce-5234-4690-d8cb-e7cefd81b1c1"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"eval_results run_qa.py sample_data\t trainer_qa.py.1 utils_qa.py.1\n",
"__pycache__ run_qa.py.1 trainer_qa.py utils_qa.py\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!mkdir eval_results"
],
"metadata": {
"id": "QDNxV31P0reW",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b84a6344-15b5-448a-d05f-e21ce39e36ec"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"mkdir: cannot create directory ‘eval_results’: File exists\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!python -m run_qa --model_name_or_path osiria/deberta-italian-question-answering --model_revision liteqa --dataset_name squad_it --do_predict --per_device_eval_batch_size=5 --output_dir eval_results"
],
"metadata": {
"id": "B5O5Lom8x_c4",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b7207826-cf61-4c92-9776-e5396a62d750"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2023-12-09 13:46:21.130219: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2023-12-09 13:46:21.130273: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2023-12-09 13:46:21.130327: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2023-12-09 13:46:23.380015: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"12/09/2023 13:46:26 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False\n",
"12/09/2023 13:46:26 - INFO - __main__ - Training/evaluation parameters TrainingArguments(\n",
"_n_gpu=1,\n",
"adafactor=False,\n",
"adam_beta1=0.9,\n",
"adam_beta2=0.999,\n",
"adam_epsilon=1e-08,\n",
"auto_find_batch_size=False,\n",
"bf16=False,\n",
"bf16_full_eval=False,\n",
"data_seed=None,\n",
"dataloader_drop_last=False,\n",
"dataloader_num_workers=0,\n",
"dataloader_persistent_workers=False,\n",
"dataloader_pin_memory=True,\n",
"ddp_backend=None,\n",
"ddp_broadcast_buffers=None,\n",
"ddp_bucket_cap_mb=None,\n",
"ddp_find_unused_parameters=None,\n",
"ddp_timeout=1800,\n",
"debug=[],\n",
"deepspeed=None,\n",
"disable_tqdm=False,\n",
"dispatch_batches=None,\n",
"do_eval=False,\n",
"do_predict=True,\n",
"do_train=False,\n",
"eval_accumulation_steps=None,\n",
"eval_delay=0,\n",
"eval_steps=None,\n",
"evaluation_strategy=no,\n",
"fp16=False,\n",
"fp16_backend=auto,\n",
"fp16_full_eval=False,\n",
"fp16_opt_level=O1,\n",
"fsdp=[],\n",
"fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},\n",
"fsdp_min_num_params=0,\n",
"fsdp_transformer_layer_cls_to_wrap=None,\n",
"full_determinism=False,\n",
"gradient_accumulation_steps=1,\n",
"gradient_checkpointing=False,\n",
"gradient_checkpointing_kwargs=None,\n",
"greater_is_better=None,\n",
"group_by_length=False,\n",
"half_precision_backend=auto,\n",
"hub_always_push=False,\n",
"hub_model_id=None,\n",
"hub_private_repo=False,\n",
"hub_strategy=every_save,\n",
"hub_token=<HUB_TOKEN>,\n",
"ignore_data_skip=False,\n",
"include_inputs_for_metrics=False,\n",
"include_num_input_tokens_seen=False,\n",
"include_tokens_per_second=False,\n",
"jit_mode_eval=False,\n",
"label_names=None,\n",
"label_smoothing_factor=0.0,\n",
"learning_rate=5e-05,\n",
"length_column_name=length,\n",
"load_best_model_at_end=False,\n",
"local_rank=0,\n",
"log_level=passive,\n",
"log_level_replica=warning,\n",
"log_on_each_node=True,\n",
"logging_dir=eval_results/runs/Dec09_13-46-26_28318cecc41c,\n",
"logging_first_step=False,\n",
"logging_nan_inf_filter=True,\n",
"logging_steps=500,\n",
"logging_strategy=steps,\n",
"lr_scheduler_kwargs={},\n",
"lr_scheduler_type=linear,\n",
"max_grad_norm=1.0,\n",
"max_steps=-1,\n",
"metric_for_best_model=None,\n",
"mp_parameters=,\n",
"neftune_noise_alpha=None,\n",
"no_cuda=False,\n",
"num_train_epochs=3.0,\n",
"optim=adamw_torch,\n",
"optim_args=None,\n",
"output_dir=eval_results,\n",
"overwrite_output_dir=False,\n",
"past_index=-1,\n",
"per_device_eval_batch_size=5,\n",
"per_device_train_batch_size=8,\n",
"prediction_loss_only=False,\n",
"push_to_hub=False,\n",
"push_to_hub_model_id=None,\n",
"push_to_hub_organization=None,\n",
"push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n",
"ray_scope=last,\n",
"remove_unused_columns=True,\n",
"report_to=['tensorboard'],\n",
"resume_from_checkpoint=None,\n",
"run_name=eval_results,\n",
"save_on_each_node=False,\n",
"save_only_model=False,\n",
"save_safetensors=True,\n",
"save_steps=500,\n",
"save_strategy=steps,\n",
"save_total_limit=None,\n",
"seed=42,\n",
"skip_memory_metrics=True,\n",
"split_batches=False,\n",
"tf32=None,\n",
"torch_compile=False,\n",
"torch_compile_backend=None,\n",
"torch_compile_mode=None,\n",
"torchdynamo=None,\n",
"tpu_metrics_debug=False,\n",
"tpu_num_cores=None,\n",
"use_cpu=False,\n",
"use_ipex=False,\n",
"use_legacy_prediction_loop=False,\n",
"use_mps_device=False,\n",
"warmup_ratio=0.0,\n",
"warmup_steps=0,\n",
"weight_decay=0.0,\n",
")\n",
"Loading Dataset Infos from /root/.cache/huggingface/modules/datasets_modules/datasets/squad_it/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71\n",
"12/09/2023 13:46:27 - INFO - datasets.info - Loading Dataset Infos from /root/.cache/huggingface/modules/datasets_modules/datasets/squad_it/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71\n",
"Overwrite dataset info from restored data version if exists.\n",
"12/09/2023 13:46:27 - INFO - datasets.builder - Overwrite dataset info from restored data version if exists.\n",
"Loading Dataset info from /root/.cache/huggingface/datasets/squad_it/default/0.1.0/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71\n",
"12/09/2023 13:46:27 - INFO - datasets.info - Loading Dataset info from /root/.cache/huggingface/datasets/squad_it/default/0.1.0/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71\n",
"Found cached dataset squad_it (/root/.cache/huggingface/datasets/squad_it/default/0.1.0/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71)\n",
"12/09/2023 13:46:27 - INFO - datasets.builder - Found cached dataset squad_it (/root/.cache/huggingface/datasets/squad_it/default/0.1.0/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71)\n",
"Loading Dataset info from /root/.cache/huggingface/datasets/squad_it/default/0.1.0/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71\n",
"12/09/2023 13:46:27 - INFO - datasets.info - Loading Dataset info from /root/.cache/huggingface/datasets/squad_it/default/0.1.0/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71\n",
"config.json: 100% 831/831 [00:00<00:00, 4.83MB/s]\n",
"[INFO|configuration_utils.py:739] 2023-12-09 13:46:27,364 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--osiria--deberta-italian-question-answering/snapshots/67dcec39da8dab0f1978c5da262349472e7dae1a/config.json\n",
"[INFO|configuration_utils.py:802] 2023-12-09 13:46:27,368 >> Model config DebertaV2Config {\n",
" \"_name_or_path\": \"osiria/deberta-italian-question-answering\",\n",
" \"architectures\": [\n",
" \"DebertaV2ForQuestionAnswering\"\n",
" ],\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"layer_norm_eps\": 1e-07,\n",
" \"max_position_embeddings\": 512,\n",
" \"max_relative_positions\": -1,\n",
" \"model_type\": \"deberta-v2\",\n",
" \"norm_rel_ebd\": \"layer_norm\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"pad_token_id\": 0,\n",
" \"pooler_dropout\": 0,\n",
" \"pooler_hidden_act\": \"gelu\",\n",
" \"pooler_hidden_size\": 768,\n",
" \"pos_att_type\": [\n",
" \"p2c\",\n",
" \"c2p\"\n",
" ],\n",
" \"position_biased_input\": false,\n",
" \"position_buckets\": 256,\n",
" \"relative_attention\": true,\n",
" \"share_att_key\": true,\n",
" \"torch_dtype\": \"float32\",\n",
" \"transformers_version\": \"4.36.0.dev0\",\n",
" \"type_vocab_size\": 0,\n",
" \"vocab_size\": 50256\n",
"}\n",
"\n",
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"tokenizer.json: 100% 2.02M/2.02M [00:00<00:00, 24.3MB/s]\n",
"[INFO|tokenization_utils_base.py:2026] 2023-12-09 13:46:27,867 >> loading file spm.model from cache at None\n",
"[INFO|tokenization_utils_base.py:2026] 2023-12-09 13:46:27,868 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--osiria--deberta-italian-question-answering/snapshots/67dcec39da8dab0f1978c5da262349472e7dae1a/tokenizer.json\n",
"[INFO|tokenization_utils_base.py:2026] 2023-12-09 13:46:27,868 >> loading file added_tokens.json from cache at None\n",
"[INFO|tokenization_utils_base.py:2026] 2023-12-09 13:46:27,868 >> loading file special_tokens_map.json from cache at None\n",
"[INFO|tokenization_utils_base.py:2026] 2023-12-09 13:46:27,868 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--osiria--deberta-italian-question-answering/snapshots/67dcec39da8dab0f1978c5da262349472e7dae1a/tokenizer_config.json\n",
"pytorch_model.bin: 100% 496M/496M [00:08<00:00, 60.4MB/s]\n",
"[INFO|modeling_utils.py:3299] 2023-12-09 13:46:36,709 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--osiria--deberta-italian-question-answering/snapshots/67dcec39da8dab0f1978c5da262349472e7dae1a/pytorch_model.bin\n",
"[INFO|modeling_utils.py:4139] 2023-12-09 13:46:38,836 >> All model checkpoint weights were used when initializing DebertaV2ForQuestionAnswering.\n",
"\n",
"[INFO|modeling_utils.py:4147] 2023-12-09 13:46:38,837 >> All the weights of DebertaV2ForQuestionAnswering were initialized from the model checkpoint at osiria/deberta-italian-question-answering.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use DebertaV2ForQuestionAnswering for predictions without further training.\n",
"Running tokenizer on prediction dataset: 0% 0/7609 [00:00<?, ? examples/s]Caching processed dataset at /root/.cache/huggingface/datasets/squad_it/default/0.1.0/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71/cache-2e8257692e3c7ae9.arrow\n",
"12/09/2023 13:46:39 - INFO - datasets.arrow_dataset - Caching processed dataset at /root/.cache/huggingface/datasets/squad_it/default/0.1.0/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71/cache-2e8257692e3c7ae9.arrow\n",
"Running tokenizer on prediction dataset: 100% 7609/7609 [00:07<00:00, 992.36 examples/s] \n",
"Downloading builder script: 100% 4.53k/4.53k [00:00<00:00, 16.7MB/s]\n",
"Downloading extra modules: 100% 3.32k/3.32k [00:00<00:00, 17.7MB/s]\n",
"12/09/2023 13:46:52 - INFO - __main__ - *** Predict ***\n",
"[INFO|trainer.py:712] 2023-12-09 13:46:52,875 >> The following columns in the test set don't have a corresponding argument in `DebertaV2ForQuestionAnswering.forward` and have been ignored: offset_mapping, example_id. If offset_mapping, example_id are not expected by `DebertaV2ForQuestionAnswering.forward`, you can safely ignore this message.\n",
"[INFO|trainer.py:3154] 2023-12-09 13:46:52,880 >> ***** Running Prediction *****\n",
"[INFO|trainer.py:3156] 2023-12-09 13:46:52,880 >> Num examples = 8565\n",
"[INFO|trainer.py:3159] 2023-12-09 13:46:52,880 >> Batch size = 5\n",
"100% 1713/1713 [04:39<00:00, 6.08it/s]12/09/2023 13:51:46 - INFO - utils_qa - Post-processing 7609 example predictions split into 8565 features.\n",
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"12/09/2023 13:52:15 - INFO - utils_qa - Saving predictions to eval_results/predict_predictions.json.\n",
"12/09/2023 13:52:15 - INFO - utils_qa - Saving nbest_preds to eval_results/predict_nbest_predictions.json.\n",
"***** predict metrics *****\n",
" predict_samples = 8565\n",
" test_exact_match = 70.1932\n",
" test_f1 = 81.017\n",
" test_runtime = 0:04:42.02\n",
" test_samples_per_second = 30.369\n",
" test_steps_per_second = 6.074\n",
"[INFO|modelcard.py:452] 2023-12-09 13:52:19,396 >> Dropping the following result as it does not have all the necessary fields:\n",
"{'task': {'name': 'Question Answering', 'type': 'question-answering'}, 'dataset': {'name': 'squad_it', 'type': 'squad_it'}}\n",
"100% 1713/1713 [05:23<00:00, 5.29it/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "ij8YkY0xyNXI"
},
"execution_count": null,
"outputs": []
}
]
} |