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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": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "02K9xO6Gvto1",
"outputId": "10556608-a497-4c01-e07d-8f95f7ec41ac"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting git+https://github.com/huggingface/transformers\n",
" Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-wnz73n3o\n",
" Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers /tmp/pip-req-build-wnz73n3o\n",
" Resolved https://github.com/huggingface/transformers to commit 12298cb65c7e9d615b749dde935a0b4966f4ae49\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==4.30.0.dev0) (3.12.0)\n",
"Collecting huggingface-hub<1.0,>=0.14.1 (from transformers==4.30.0.dev0)\n",
" Downloading huggingface_hub-0.15.1-py3-none-any.whl (236 kB)\n",
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"Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers==4.30.0.dev0)\n",
" Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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"\u001b[?25hCollecting safetensors>=0.3.1 (from transformers==4.30.0.dev0)\n",
" Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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"Building wheels for collected packages: transformers\n",
" Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for transformers: filename=transformers-4.30.0.dev0-py3-none-any.whl size=7166227 sha256=fdfdc040a7686aacafeef0a3fbf5d6b894a22a4967949ffa981ee09e9f574aac\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-mynbfxj2/wheels/c0/14/d6/6c9a5582d2ac191ec0a483be151a4495fe1eb2a6706ca49f1b\n",
"Successfully built transformers\n",
"Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
"Successfully installed huggingface-hub-0.15.1 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.30.0.dev0\n"
]
}
],
"source": [
"!pip install git+https://github.com/huggingface/transformers"
]
},
{
"cell_type": "code",
"source": [
"!pip install datasets"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VKgybrUswa1q",
"outputId": "7f2ad166-68a2-433c-d0b4-8312192dee9e"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting datasets\n",
" Downloading datasets-2.12.0-py3-none-any.whl (474 kB)\n",
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"Collecting dill<0.3.7,>=0.3.0 (from datasets)\n",
" Downloading dill-0.3.6-py3-none-any.whl (110 kB)\n",
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"Collecting xxhash (from datasets)\n",
" Downloading xxhash-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
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"Installing collected packages: xxhash, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, aiosignal, aiohttp, datasets\n",
"Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.12.0 dill-0.3.6 frozenlist-1.3.3 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0 yarl-1.9.2\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install accelerate"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xYEW0Hjqz2lo",
"outputId": "773d7aa2-0675-4edf-cb7e-34046a4fba55"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting accelerate\n",
" Downloading accelerate-0.20.0-py3-none-any.whl (227 kB)\n",
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"Installing collected packages: accelerate\n",
"Successfully installed accelerate-0.20.0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install evaluate"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9OFz3JdR0cQ_",
"outputId": "202b4d5c-8d48-4f01-c760-9bfb413fbb30"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting evaluate\n",
" Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
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"Installing collected packages: evaluate\n",
"Successfully installed evaluate-0.4.0\n"
]
}
]
},
{
"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": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JJhpBNwpxOTK",
"outputId": "bbb60e1c-bde4-49b0-bfb2-eaf01cbcad29"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2023-06-07 16:47:01-- https://raw.githubusercontent.com/huggingface/transformers/main/examples/pytorch/question-answering/run_qa.py\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
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"\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": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Loa6y9XqzXJ4",
"outputId": "3be368ce-5d9e-4df2-c3c9-f6a5a537c405"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"run_qa.py sample_data\ttrainer_qa.py utils_qa.py\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!mkdir eval_results"
],
"metadata": {
"id": "QDNxV31P0reW"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!python -m run_qa --model_name_or_path osiria/deberta-italian-question-answering --dataset_name squad_it --do_predict --per_device_eval_batch_size=5 --output_dir eval_results"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "B5O5Lom8x_c4",
"outputId": "ebb24d93-3e0c-4eae-ca30-28d1c9a46bc6"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2023-06-07 16:47:10.607285: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"06/07/2023 16:47:13 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False\n",
"06/07/2023 16:47:13 - 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_pin_memory=True,\n",
"ddp_backend=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",
"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={'fsdp_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",
"greater_is_better=None,\n",
"group_by_length=False,\n",
"half_precision_backend=auto,\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",
"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/Jun07_16-47-13_0d4f01f1a5fc,\n",
"logging_first_step=False,\n",
"logging_nan_inf_filter=True,\n",
"logging_steps=500,\n",
"logging_strategy=steps,\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",
"no_cuda=False,\n",
"num_train_epochs=3.0,\n",
"optim=adamw_hf,\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_safetensors=False,\n",
"save_steps=500,\n",
"save_strategy=steps,\n",
"save_total_limit=None,\n",
"seed=42,\n",
"sharded_ddp=[],\n",
"skip_memory_metrics=True,\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_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",
"xpu_backend=None,\n",
")\n",
"06/07/2023 16:47:13 - INFO - datasets.utils.file_utils - https://huggingface.co/datasets/squad_it/resolve/main/squad_it.py not found in cache or force_download set to True, downloading to /root/.cache/huggingface/datasets/downloads/tmp6fvibkw8\n",
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"06/07/2023 16:47:14 - INFO - datasets.utils.file_utils - storing https://huggingface.co/datasets/squad_it/resolve/main/squad_it.py in cache at /root/.cache/huggingface/datasets/downloads/2f7a246f661e6acb3a9ad072ed14241ea343265e7d863b88717897941c3ab1dd.671c3c3daf2f05e0355b498b0f5a7f2167fa94dc6de36195fe1ba0503e6dcc30.py\n",
"06/07/2023 16:47:14 - INFO - datasets.utils.file_utils - creating metadata file for /root/.cache/huggingface/datasets/downloads/2f7a246f661e6acb3a9ad072ed14241ea343265e7d863b88717897941c3ab1dd.671c3c3daf2f05e0355b498b0f5a7f2167fa94dc6de36195fe1ba0503e6dcc30.py\n",
"06/07/2023 16:47:14 - INFO - datasets.utils.file_utils - https://huggingface.co/datasets/squad_it/resolve/main/dataset_infos.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/datasets/downloads/tmpax6jsbgs\n",
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"06/07/2023 16:47:14 - INFO - datasets.utils.file_utils - storing https://huggingface.co/datasets/squad_it/resolve/main/dataset_infos.json in cache at /root/.cache/huggingface/datasets/downloads/f6d7650a1e4a9f0e42b54f34ac614f7c1142ad26c1bd19a448eb606fe09f7515.b459489a7b1a4d859decbeef851034c7846ef6cce8ec2cf192cf4b4f40d0865d\n",
"06/07/2023 16:47:14 - INFO - datasets.utils.file_utils - creating metadata file for /root/.cache/huggingface/datasets/downloads/f6d7650a1e4a9f0e42b54f34ac614f7c1142ad26c1bd19a448eb606fe09f7515.b459489a7b1a4d859decbeef851034c7846ef6cce8ec2cf192cf4b4f40d0865d\n",
"06/07/2023 16:47:14 - INFO - datasets.utils.file_utils - https://huggingface.co/datasets/squad_it/resolve/main/README.md not found in cache or force_download set to True, downloading to /root/.cache/huggingface/datasets/downloads/tmpmy7om6r1\n",
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"06/07/2023 16:47:14 - INFO - datasets.utils.file_utils - storing https://huggingface.co/datasets/squad_it/resolve/main/README.md in cache at /root/.cache/huggingface/datasets/downloads/b664892e5965a1ef2e173eadc7e9cbf6ad4aeb6240d7b3bb6ab9f32850363a6f.71fe7dbf5f3b574d85b57b5a722ebc49fe8ec8b03d655344891fb1b05c4634fb\n",
"06/07/2023 16:47:14 - INFO - datasets.utils.file_utils - creating metadata file for /root/.cache/huggingface/datasets/downloads/b664892e5965a1ef2e173eadc7e9cbf6ad4aeb6240d7b3bb6ab9f32850363a6f.71fe7dbf5f3b574d85b57b5a722ebc49fe8ec8b03d655344891fb1b05c4634fb\n",
"06/07/2023 16:47:14 - INFO - datasets.info - Loading Dataset Infos from /root/.cache/huggingface/modules/datasets_modules/datasets/squad_it/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71\n",
"06/07/2023 16:47:14 - INFO - datasets.builder - Generating dataset squad_it (/root/.cache/huggingface/datasets/squad_it/default/0.1.0/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71)\n",
"Downloading and preparing dataset squad_it/default to /root/.cache/huggingface/datasets/squad_it/default/0.1.0/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71...\n",
"06/07/2023 16:47:14 - INFO - datasets.builder - Dataset not on Hf google storage. Downloading and preparing it from source\n",
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"06/07/2023 16:47:16 - INFO - datasets.builder - Generating train split\n",
"06/07/2023 16:47:22 - INFO - datasets.builder - Generating test split\n",
"06/07/2023 16:47:23 - INFO - datasets.utils.info_utils - All the splits matched successfully.\n",
"Dataset squad_it downloaded and prepared to /root/.cache/huggingface/datasets/squad_it/default/0.1.0/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71. Subsequent calls will reuse this data.\n",
"100% 2/2 [00:00<00:00, 542.64it/s]\n",
"Downloading (…)lve/main/config.json: 100% 826/826 [00:00<00:00, 5.76MB/s]\n",
"[INFO|configuration_utils.py:669] 2023-06-07 16:47:23,846 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--osiria--deberta-italian-question-answering/snapshots/e93341adad1d2140bacd231c54d7c3e0fd1317c3/config.json\n",
"[INFO|configuration_utils.py:725] 2023-06-07 16:47:23,850 >> 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\": \"4.30.0.dev0\",\n",
" \"type_vocab_size\": 0,\n",
" \"vocab_size\": 50256\n",
"}\n",
"\n",
"Downloading (…)okenizer_config.json: 100% 412/412 [00:00<00:00, 2.41MB/s]\n",
"Downloading (…)/main/tokenizer.json: 100% 2.02M/2.02M [00:00<00:00, 12.6MB/s]\n",
"[INFO|tokenization_utils_base.py:1823] 2023-06-07 16:47:24,622 >> loading file spm.model from cache at None\n",
"[INFO|tokenization_utils_base.py:1823] 2023-06-07 16:47:24,622 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--osiria--deberta-italian-question-answering/snapshots/e93341adad1d2140bacd231c54d7c3e0fd1317c3/tokenizer.json\n",
"[INFO|tokenization_utils_base.py:1823] 2023-06-07 16:47:24,622 >> loading file added_tokens.json from cache at None\n",
"[INFO|tokenization_utils_base.py:1823] 2023-06-07 16:47:24,622 >> loading file special_tokens_map.json from cache at None\n",
"[INFO|tokenization_utils_base.py:1823] 2023-06-07 16:47:24,622 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--osiria--deberta-italian-question-answering/snapshots/e93341adad1d2140bacd231c54d7c3e0fd1317c3/tokenizer_config.json\n",
"Downloading pytorch_model.bin: 100% 496M/496M [00:07<00:00, 66.0MB/s]\n",
"[INFO|modeling_utils.py:2578] 2023-06-07 16:47:32,789 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--osiria--deberta-italian-question-answering/snapshots/e93341adad1d2140bacd231c54d7c3e0fd1317c3/pytorch_model.bin\n",
"[INFO|modeling_utils.py:3295] 2023-06-07 16:47:34,943 >> All model checkpoint weights were used when initializing DebertaV2ForQuestionAnswering.\n",
"\n",
"[INFO|modeling_utils.py:3303] 2023-06-07 16:47:34,943 >> 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]06/07/2023 16:47:36 - INFO - datasets.arrow_dataset - Caching processed dataset at /root/.cache/huggingface/datasets/squad_it/default/0.1.0/d442bdb4794b4bae227ab19105b76d706ed7cf2ac342e4c9da4a5c36bde19d71/cache-c70dd3f912ae21c4.arrow\n",
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"06/07/2023 16:47:48 - INFO - __main__ - *** Predict ***\n",
"[INFO|trainer.py:774] 2023-06-07 16:47:48,896 >> 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:3163] 2023-06-07 16:47:48,899 >> ***** Running Prediction *****\n",
"[INFO|trainer.py:3165] 2023-06-07 16:47:48,899 >> Num examples = 8565\n",
"[INFO|trainer.py:3168] 2023-06-07 16:47:48,899 >> Batch size = 5\n",
"100% 1713/1713 [04:44<00:00, 5.97it/s]06/07/2023 16:52:48 - INFO - utils_qa - Post-processing 7609 example predictions split into 8565 features.\n",
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"06/07/2023 16:53:17 - INFO - utils_qa - Saving predictions to eval_results/predict_predictions.json.\n",
"06/07/2023 16:53:17 - INFO - utils_qa - Saving nbest_preds to eval_results/predict_nbest_predictions.json.\n",
"***** predict metrics *****\n",
" predict_samples = 8565\n",
" test_exact_match = 70.0486\n",
" test_f1 = 80.9773\n",
" test_runtime = 0:04:47.64\n",
" test_samples_per_second = 29.777\n",
" test_steps_per_second = 5.955\n",
"[INFO|modelcard.py:451] 2023-06-07 16:53:20,099 >> 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 [05:28<00:00, 5.22it/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "ij8YkY0xyNXI"
},
"execution_count": null,
"outputs": []
}
]
} |