{ "cells": [ { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "from src import BertForSemanticEmbedding, getLabelModel\n", "from src import DataTrainingArguments, ModelArguments, CustomTrainingArguments, read_yaml_config\n", "from src import dataset_classification_type\n", "from src import SemSupDataset\n", "from transformers import AutoConfig, HfArgumentParser, AutoTokenizer\n", "import torch\n", "\n", "import json\n", "from tqdm import tqdm" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "ARGS_FILE = 'configs/ablation_amzn_eda.yml'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Yaml Config is:\n", "--------------------------------------------------------------------------------\n", "{'task_name': 'amazon13k', 'dataset_name': 'amazon13k', 'dataset_config_name': None, 'max_seq_length': 160, 'overwrite_output_dir': False, 'overwrite_cache': False, 'pad_to_max_length': True, 'load_from_local': True, 'max_train_samples': None, 'max_eval_samples': 15000, 'max_predict_samples': None, 'train_file': '/n/fs/nlp-pranjal/SemSup-LMLC/training/datasets/Amzn13K/train_split6500_2.jsonl', 'validation_file': '/n/fs/nlp-pranjal/SemSup-LMLC/training/datasets/Amzn13K/test_unseen_split6500_2.jsonl', 'test_file': '/n/fs/nlp-pranjal/SemSup-LMLC/training/datasets/Amzn13K/test_unseen_split6500_2.jsonl', 'label_max_seq_length': 160, 'descriptions_file': '/n/fs/nlp-pranjal/SemSup-LMLC/training/datasets/Amzn13K/heir_withdescriptions_v3_v3_unseen_edaaug.json', 'test_descriptions_file': '/n/fs/nlp-pranjal/SemSup-LMLC/training/datasets/Amzn13K/heir_withdescriptions_v3_v3.json', 'all_labels': '/n/fs/nlp-pranjal/SemSup-LMLC/training/datasets/Amzn13K/all_labels.txt', 'test_labels': '/n/fs/nlp-pranjal/SemSup-LMLC/training/datasets/Amzn13K/unseen_labels_split6500_2.txt', 'contrastive_learning_samples': 1000, 'cl_min_positive_descs': 1, 'coil_cluster_mapping_path': 'bert_coil_map_dict_lemma255K_isotropic.json', 'model_name_or_path': 'bert-base-uncased', 'config_name': None, 'tokenizer_name': None, 'cache_dir': None, 'use_fast_tokenizer': True, 'model_revision': 'main', 'use_auth_token': False, 'ignore_mismatched_sizes': False, 'negative_sampling': 'none', 'semsup': True, 'label_model_name_or_path': 'prajjwal1/bert-small', 'encoder_model_type': 'bert', 'use_custom_optimizer': 'adamw', 'output_learning_rate': 0.0001, 'arch_type': 2, 'add_label_name': True, 'normalize_embeddings': False, 'tie_weights': False, 'coil': True, 'colbert': False, 'token_dim': 16, 'label_frozen_layers': 2, 'do_train': True, 'do_eval': True, 'do_predict': False, 'per_device_train_batch_size': 1, 'gradient_accumulation_steps': 8, 'per_device_eval_batch_size': 1, 'learning_rate': 5e-05, 'num_train_epochs': 2, 'save_steps': 4900, 'evaluation_strategy': 'steps', 'eval_steps': 3000000, 'fp16': True, 'fp16_opt_level': 'O1', 'lr_scheduler_type': 'linear', 'dataloader_num_workers': 16, 'label_names': ['labels'], 'scenario': 'unseen_labels', 'ddp_find_unused_parameters': False, 'ignore_data_skip': True, 'seed': -1, 'EXP_NAME': 'semsup_descs_100ep_newds_cosine', 'EXP_DESC': 'SemSup Descriptions ran for 100 epochs', 'output_dir': 'demo_tmp'}\n", "--------------------------------------------------------------------------------\n" ] } ], "source": [ "parser = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments))\n", "model_args, data_args, training_args = parser.parse_dict(read_yaml_config(ARGS_FILE, output_dir = 'demo_tmp', extra_args = {}))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "config = AutoConfig.from_pretrained(\n", " model_args.config_name if model_args.config_name else model_args.model_name_or_path,\n", " finetuning_task=data_args.task_name,\n", " cache_dir=model_args.cache_dir,\n", " revision=model_args.model_revision,\n", " use_auth_token=True if model_args.use_auth_token else None,\n", ")\n", "\n", "config.model_name_or_path = model_args.model_name_or_path\n", "config.problem_type = dataset_classification_type[data_args.task_name]\n", "config.negative_sampling = model_args.negative_sampling\n", "config.semsup = model_args.semsup\n", "config.encoder_model_type = model_args.encoder_model_type\n", "config.arch_type = model_args.arch_type\n", "config.coil = model_args.coil\n", "config.token_dim = model_args.token_dim\n", "config.colbert = model_args.colbert" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Some weights of the model checkpoint at prajjwal1/bert-small were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias']\n", "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias']\n", "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Config is BertConfig {\n", " \"_name_or_path\": \"bert-base-uncased\",\n", " \"arch_type\": 2,\n", " \"architectures\": [\n", " \"BertForMaskedLM\"\n", " ],\n", " \"attention_probs_dropout_prob\": 0.1,\n", " \"classifier_dropout\": null,\n", " \"coil\": true,\n", " \"colbert\": false,\n", " \"encoder_model_type\": \"bert\",\n", " \"finetuning_task\": \"amazon13k\",\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout_prob\": 0.1,\n", " \"hidden_size\": 768,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"label_hidden_size\": 512,\n", " \"layer_norm_eps\": 1e-12,\n", " \"max_position_embeddings\": 512,\n", " \"model_name_or_path\": \"bert-base-uncased\",\n", " \"model_type\": \"bert\",\n", " \"negative_sampling\": \"none\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 12,\n", " \"pad_token_id\": 0,\n", " \"position_embedding_type\": \"absolute\",\n", " \"problem_type\": \"multi_label_classification\",\n", " \"semsup\": true,\n", " \"token_dim\": 16,\n", " \"transformers_version\": \"4.20.0\",\n", " \"type_vocab_size\": 2,\n", " \"use_cache\": true,\n", " \"vocab_size\": 30522\n", "}\n", "\n" ] }, { "data": { "text/html": [ "
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
       "│ /tmp/ipykernel_1149757/433561231.py:6 in <cell line: 6>                                          │\n",
       "│                                                                                                  │\n",
       "│ [Errno 2] No such file or directory: '/tmp/ipykernel_1149757/433561231.py'                       │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
       "NameError: name 'label_list' is not defined\n",
       "
\n" ], "text/plain": [ "\u001b[31m╭─\u001b[0m\u001b[31m────────────────────────────── \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m ───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[2;33m/tmp/ipykernel_1149757/\u001b[0m\u001b[1;33m433561231.py\u001b[0m:\u001b[94m6\u001b[0m in \u001b[92m\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m│\u001b[0m \u001b[3;31m[Errno 2] No such file or directory: '/tmp/ipykernel_1149757/433561231.py'\u001b[0m \u001b[31m│\u001b[0m\n", "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n", "\u001b[1;91mNameError: \u001b[0mname \u001b[32m'label_list'\u001b[0m is not defined\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "label_model, label_tokenizer = getLabelModel(data_args, model_args)\n", "config.label_hidden_size = label_model.config.hidden_size\n", "model = BertForSemanticEmbedding(config)\n", "model.label_model = label_model\n", "model.label_tokenizer = label_tokenizer\n", "model.config.label2id = {l: i for i, l in enumerate(label_list)}\n", "model.config.id2label = {id: label for label, id in config.label2id.items()}" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "coil_cluster_map = json.load(open(''))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "max_descs_per_label = 20" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "def tokenize_class_descs(label_descs, return_lengths = False):\n", " if return_lengths == 1:\n", " return {\n", " label_key: min(descs[0],max_descs_per_label) for label_key, descs in label_descs.items() \n", " } # descs 0 is the length\n", " else:\n", " return {\n", " label_key: descs[1][:max_descs_per_label] for label_key, descs in label_descs.items() \n", " }\n", " \n", "def save_tokenized_descs(add_label_name = False):\n", " class_descs_tokenized = dict()\n", " for label_key in tqdm(list(class_descs.keys())):\n", " descs_len = class_descs_len[label_key]\n", " descs = class_descs[label_key]\n", " class_descs_tokenized[label_key] = tokenizer(\n", " [label_key + \". \" + x for x in descs] if add_label_name else\n", " descs,\n", " max_length = data_args.label_max_seq_length, padding = 'max_length', truncation= True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "js_file = json.load(open(data_args.test_descriptions_file, encoding = 'utf-8'))\n", "class_descs_len = tokenize_class_descs(js_file, return_lengths = True)\n", "class_descs = tokenize_class_descs(js_file)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 13330/13330 [00:23<00:00, 555.67it/s]\n" ] } ], "source": [ "tokenized_descs = save_tokenized_descs(model_args.add_label_name)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "class DummyDset:\n", " def __init__(self, inp_text = None):\n", " self.data = [] \n", " if inp_text is not None:\n", " self.set_text(inp_text)\n", "\n", " def set_text(self, tex):\n", " result = tokenizer(tex, padding='max_length', max_length=data_args.max_seq_length, truncation=True)\n", " result['label'] = [0 for x in range(len(label_list))]\n", " self.data = [result]\n", "\n", " def __len__(self,):\n", " return 1\n", "\n", " def __getitem__(self, idx):\n", " return self.data[0]" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "input_dset = DummyDset()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "label_list = [x.strip() for x in open(data_args.all_labels).readlines()]\n", "\n", "model.config.label2id = {l: i for i, l in enumerate(label_list)}\n", "model.config.id2label = {id: label for label, id in config.label2id.items()}\n", "\n", "id2label = model.config.id2label\n", "label_to_id = model.config.label2id\n", "\n", "test_labels = [x.strip() for x in open(data_args.test_labels).readlines()]\n", "class_descs_tokenized = None" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dset.semsup" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 13330/13330 [00:50<00:00, 263.17it/s]\n" ] } ], "source": [ "dset = SemSupDataset(input_dset, data_args, data_args.test_descriptions_file, label_to_id, id2label, label_tokenizer, return_desc_embeddings=True, seen_labels = None if training_args.scenario == 'seen' else test_labels, add_label_name = model_args.add_label_name, max_descs_per_label = data_args.max_descs_per_label, use_precomputed_embeddings = model_args.use_precomputed_embeddings, class_descs_tokenized = class_descs_tokenized, isTrain = False)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dset)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "for item in dset:\n", " break" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "dset.input_dataset.set_text(\"Hello this is a text dataset\")" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "device = 'cuda' if torch.cuda.is_available() else 'cpu'" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BertForSemanticEmbedding(\n", " (encoder): BertModel(\n", " (embeddings): BertEmbeddings(\n", " (word_embeddings): Embedding(30522, 768, padding_idx=0)\n", " (position_embeddings): Embedding(512, 768)\n", " (token_type_embeddings): Embedding(2, 768)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (encoder): BertEncoder(\n", " (layer): ModuleList(\n", " (0): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " (intermediate_act_fn): GELUActivation()\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (1): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " (intermediate_act_fn): GELUActivation()\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (2): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " (intermediate_act_fn): GELUActivation()\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (3): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " (intermediate_act_fn): GELUActivation()\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (4): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " (intermediate_act_fn): GELUActivation()\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (5): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): 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Linear(in_features=512, out_features=2048, bias=True)\n", " (intermediate_act_fn): GELUActivation()\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=2048, out_features=512, bias=True)\n", " (LayerNorm): LayerNorm((512,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " )\n", " (pooler): BertPooler(\n", " (dense): Linear(in_features=512, out_features=512, bias=True)\n", " (activation): Tanh()\n", " )\n", " )\n", ")" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.to(device)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[ 101, 7592, 2023, 2003, 1037, 3793, 2951, 13462, 102, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n", " device='cuda:0')" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "item['input_ids']" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "dset.input_dataset.set_text('Hello how is it going?')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dset.input_dataset.__getitem__(0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.eval()\n", "with torch.no_grad():\n", " for item in dset:\n", " print('hah')\n", " item = {k:torch.tensor(v, device = device).unsqueeze(0) for k,v in item.items()}\n", " del item['label']\n", " logits = model(**item)\n", " print('Lets go')" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
       "│ /tmp/ipykernel_1149757/1661601364.py:1 in <cell line: 1>                                         │\n",
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       "│ [Errno 2] No such file or directory: '/tmp/ipykernel_1149757/1661601364.py'                      │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
       "NameError: name 'logits' is not defined\n",
       "
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