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32c0ef6
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1 Parent(s): f030d3a

fixes

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Files changed (3) hide show
  1. config/config.yaml +1 -1
  2. research/05_Model_evaluation.ipynb +94 -9
  3. src/textSummarizer/components/model_trainer.py +2 -1
config/config.yaml CHANGED
@@ -31,4 +31,4 @@ model_evaluation:
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  data_path: artifacts/data_transformation/samsum_dataset
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  model_path: artifacts/model_trainer/pegasus-samsum_model
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  tokenizer_path: artifacts/model_trainer/tokenizer_name
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- metrics_file_name: artifacts/model_evaluation/metrics.csv
 
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  data_path: artifacts/data_transformation/samsum_dataset
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  model_path: artifacts/model_trainer/pegasus-samsum_model
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  tokenizer_path: artifacts/model_trainer/tokenizer_name
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+ metric_file_name: artifacts/model_evaluation/metrics.csv
research/05_Model_evaluation.ipynb CHANGED
@@ -55,7 +55,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 7,
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  "id": "ffb2ded1",
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  "metadata": {},
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  "outputs": [],
@@ -71,7 +71,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 8,
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  "id": "4cbe6e7f",
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  "metadata": {},
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  "outputs": [],
@@ -82,7 +82,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 9,
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  "id": "6ef66517",
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  "metadata": {},
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  "outputs": [],
@@ -117,7 +117,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 10,
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  "id": "98b6adaf",
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  "metadata": {},
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  "outputs": [
@@ -133,13 +133,14 @@
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  "name": "stdout",
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  "output_type": "stream",
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  "text": [
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- "[2025-07-02 02:05:06,043: INFO: config : PyTorch version 2.5.1+cu121 available. ]\n"
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  ]
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  }
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  ],
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  "source": [
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  "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
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- "from datasets import load_dataset, load_from_disk\n",
 
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  "import torch\n",
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  "import pandas as pd\n",
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  "from tqdm import tqdm"
@@ -147,7 +148,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": null,
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  "id": "ac50b978",
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  "metadata": {},
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  "outputs": [],
@@ -177,12 +178,96 @@
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  " inputs = tokenizer( article_batch, max_length=1024, truncation=True,\n",
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  " padding=\"max_length\", return_tensors=\"pt\")\n",
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  " \n",
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- " s\n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  " \n",
 
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  " \n",
 
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  " \n",
 
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  " \n",
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- " "
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ]
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  }
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  ],
 
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": 5,
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  "id": "ffb2ded1",
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  "metadata": {},
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  "outputs": [],
 
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": 6,
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  "id": "4cbe6e7f",
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  "metadata": {},
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  "outputs": [],
 
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": 7,
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  "id": "6ef66517",
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  "metadata": {},
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  "outputs": [],
 
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": 8,
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  "id": "98b6adaf",
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  "metadata": {},
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  "outputs": [
 
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  "name": "stdout",
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  "output_type": "stream",
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  "text": [
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+ "[2025-07-02 03:44:43,649: INFO: config : PyTorch version 2.5.1+cu121 available. ]\n"
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  ]
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  }
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  ],
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  "source": [
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  "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
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+ "from datasets import load_from_disk, load_dataset\n",
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+ "import evaluate\n",
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  "import torch\n",
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  "import pandas as pd\n",
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  "from tqdm import tqdm"
 
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": 9,
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  "id": "ac50b978",
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  "metadata": {},
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  "outputs": [],
 
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  " inputs = tokenizer( article_batch, max_length=1024, truncation=True,\n",
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  " padding=\"max_length\", return_tensors=\"pt\")\n",
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  " \n",
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+ " summaries = model.generate(inputs[\"input_ids\"].todevice(device),\n",
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+ " attention_mask=inputs[\"attention_mask\"].todevice(device),\n",
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+ " Length_penalty=0.8, num_beams=8, max_length=128)\n",
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+ " ''' parameter for length penalty ensures that model does not generate sequences that are too long '''\n",
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+ " # Finally, we decode the generated texts,\n",
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+ " # replace the token and add the decoded texts with the references to the metrics\n",
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+ " \n",
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+ " decoded_summaries = [tokenizer.batch_decode(s, skip_special_tokens=True,\n",
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+ " clean_up_tokenization_spaces= True) \n",
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+ " for s in summaries]\n",
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+ " \n",
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+ " decoded_summaries = [d.replace(\"\", \" \") for d in decoded_summaries]\n",
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+ " \n",
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+ " metric.add_batch(predictions=decoded_summaries, references=target_batch)\n",
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+ " \n",
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+ " score = metric.compute()\n",
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+ " return score \n",
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+ " \n",
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+ " def evaluate(self):\n",
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+ " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
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+ " tokenizer = AutoTokenizer.from_pretrained(self.config.tokenizer_path)\n",
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+ " model_pegasus = AutoModelForSeq2SeqLM.from_pretrained(self.config.model_path).to(device)\n",
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  " \n",
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+ " dataset_samsum_pt = load_from_disk(self.config.data_path)\n",
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  " \n",
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+ " rouge_names = [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"]\n",
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  " \n",
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+ " rouge_metric = evaluate.load(\"rouge\")\n",
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  " \n",
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+ " score = self.calculate_metric_on_test_ds(\n",
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+ " dataset_samsum_pt['test'][0:10], rouge_metric, model_pegasus, tokenizer, batch_size = 2, column_text = 'dialogue', column_summary= 'summary')\n",
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+ " \n",
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+ " rouge_dict = dict((rn, score[rn].mid.fmeasure ) for rn in rouge_names )\n",
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+ "\n",
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+ " df = pd.DataFrame(rouge_dict, index = ['pegasus'] )\n",
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+ " df.to_csv(self.config.metric_file_name, index=False)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 10,
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+ "id": "54baa53e",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "[2025-07-02 03:44:49,371: INFO: common : yaml. file: config\\config.yaml loaded successfully. ]\n",
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+ "[2025-07-02 03:44:49,375: INFO: common : yaml. file: params.yaml loaded successfully. ]\n",
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+ "[2025-07-02 03:44:49,375: INFO: common : created directory at path : artifacts_root ]\n",
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+ "[2025-07-02 03:44:49,375: INFO: common : created directory at path : artifacts/model_evaluation ]\n"
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+ ]
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+ },
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+ {
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+ "ename": "HFValidationError",
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+ "evalue": "Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'artifacts/model_trainer/tokenizer_name'. Use `repo_type` argument if needed.",
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+ "output_type": "error",
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+ "traceback": [
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+ "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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+ "\u001b[31mHFValidationError\u001b[39m Traceback (most recent call last)",
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+ "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jeeva\\.conda\\envs\\textSummarizer\\Lib\\site-packages\\transformers\\utils\\hub.py:470\u001b[39m, in \u001b[36mcached_files\u001b[39m\u001b[34m(path_or_repo_id, filenames, 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[39m\n\u001b[32m 468\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(full_filenames) == \u001b[32m1\u001b[39m:\n\u001b[32m 469\u001b[39m \u001b[38;5;66;03m# This is slightly better for only 1 file\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m470\u001b[39m \u001b[43mhf_hub_download\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 471\u001b[39m \u001b[43m \u001b[49m\u001b[43mpath_or_repo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 472\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilenames\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 473\u001b[39m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 474\u001b[39m \u001b[43m \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 475\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 476\u001b[39m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 477\u001b[39m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m=\u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 478\u001b[39m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 479\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 480\u001b[39m \u001b[43m \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 481\u001b[39m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 482\u001b[39m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 483\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 484\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
243
+ "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jeeva\\.conda\\envs\\textSummarizer\\Lib\\site-packages\\huggingface_hub\\utils\\_validators.py:106\u001b[39m, in \u001b[36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 105\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m arg_name \u001b[38;5;129;01min\u001b[39;00m [\u001b[33m\"\u001b[39m\u001b[33mrepo_id\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mfrom_id\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mto_id\u001b[39m\u001b[33m\"\u001b[39m]:\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m \u001b[43mvalidate_repo_id\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 108\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m arg_name == \u001b[33m\"\u001b[39m\u001b[33mtoken\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m arg_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
244
+ "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jeeva\\.conda\\envs\\textSummarizer\\Lib\\site-packages\\huggingface_hub\\utils\\_validators.py:154\u001b[39m, in \u001b[36mvalidate_repo_id\u001b[39m\u001b[34m(repo_id)\u001b[39m\n\u001b[32m 153\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m repo_id.count(\u001b[33m\"\u001b[39m\u001b[33m/\u001b[39m\u001b[33m\"\u001b[39m) > \u001b[32m1\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m154\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m HFValidationError(\n\u001b[32m 155\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mRepo id must be in the form \u001b[39m\u001b[33m'\u001b[39m\u001b[33mrepo_name\u001b[39m\u001b[33m'\u001b[39m\u001b[33m or \u001b[39m\u001b[33m'\u001b[39m\u001b[33mnamespace/repo_name\u001b[39m\u001b[33m'\u001b[39m\u001b[33m:\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 156\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m. Use `repo_type` argument if needed.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 157\u001b[39m )\n\u001b[32m 159\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m REPO_ID_REGEX.match(repo_id):\n",
245
+ "\u001b[31mHFValidationError\u001b[39m: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'artifacts/model_trainer/tokenizer_name'. Use `repo_type` argument if needed.",
246
+ "\nDuring handling of the above exception, another exception occurred:\n",
247
+ "\u001b[31mHFValidationError\u001b[39m Traceback (most recent call last)",
248
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m 5\u001b[39m model_evaluation_config.evaluate()\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m e\n",
249
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 3\u001b[39m model_evaluation_config = config.get_model_evaluation_config()\n\u001b[32m 4\u001b[39m model_evaluation_config = ModelEvaluation(config=model_evaluation_config)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[43mmodel_evaluation_config\u001b[49m\u001b[43m.\u001b[49m\u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m e\n",
250
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 46\u001b[39m, in \u001b[36mModelEvaluation.evaluate\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 44\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mevaluate\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m 45\u001b[39m device = \u001b[33m\"\u001b[39m\u001b[33mcuda\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch.cuda.is_available() \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mcpu\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m46\u001b[39m tokenizer = \u001b[43mAutoTokenizer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtokenizer_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 47\u001b[39m model_pegasus = AutoModelForSeq2SeqLM.from_pretrained(\u001b[38;5;28mself\u001b[39m.config.model_path).to(device)\n\u001b[32m 49\u001b[39m dataset_samsum_pt = load_from_disk(\u001b[38;5;28mself\u001b[39m.config.data_path)\n",
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+ "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jeeva\\.conda\\envs\\textSummarizer\\Lib\\site-packages\\transformers\\models\\auto\\tokenization_auto.py:982\u001b[39m, in \u001b[36mAutoTokenizer.from_pretrained\u001b[39m\u001b[34m(cls, pretrained_model_name_or_path, *inputs, **kwargs)\u001b[39m\n\u001b[32m 979\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)\n\u001b[32m 981\u001b[39m \u001b[38;5;66;03m# Next, let's try to use the tokenizer_config file to get the tokenizer class.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m982\u001b[39m tokenizer_config = \u001b[43mget_tokenizer_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 983\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33m_commit_hash\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m tokenizer_config:\n\u001b[32m 984\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33m_commit_hash\u001b[39m\u001b[33m\"\u001b[39m] = tokenizer_config[\u001b[33m\"\u001b[39m\u001b[33m_commit_hash\u001b[39m\u001b[33m\"\u001b[39m]\n",
252
+ "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jeeva\\.conda\\envs\\textSummarizer\\Lib\\site-packages\\transformers\\models\\auto\\tokenization_auto.py:814\u001b[39m, in \u001b[36mget_tokenizer_config\u001b[39m\u001b[34m(pretrained_model_name_or_path, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, **kwargs)\u001b[39m\n\u001b[32m 811\u001b[39m token = use_auth_token\n\u001b[32m 813\u001b[39m commit_hash = kwargs.get(\u001b[33m\"\u001b[39m\u001b[33m_commit_hash\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m814\u001b[39m resolved_config_file = \u001b[43mcached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 815\u001b[39m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 816\u001b[39m \u001b[43m \u001b[49m\u001b[43mTOKENIZER_CONFIG_FILE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 817\u001b[39m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 818\u001b[39m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 819\u001b[39m \u001b[43m \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 820\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 821\u001b[39m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 822\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 823\u001b[39m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 824\u001b[39m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m=\u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 825\u001b[39m \u001b[43m \u001b[49m\u001b[43m_raise_exceptions_for_gated_repo\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 826\u001b[39m \u001b[43m \u001b[49m\u001b[43m_raise_exceptions_for_missing_entries\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 827\u001b[39m \u001b[43m \u001b[49m\u001b[43m_raise_exceptions_for_connection_errors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 828\u001b[39m \u001b[43m \u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcommit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 829\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 830\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m resolved_config_file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 831\u001b[39m logger.info(\u001b[33m\"\u001b[39m\u001b[33mCould not locate the tokenizer configuration file, will try to use the model config instead.\u001b[39m\u001b[33m\"\u001b[39m)\n",
253
+ "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jeeva\\.conda\\envs\\textSummarizer\\Lib\\site-packages\\transformers\\utils\\hub.py:312\u001b[39m, in \u001b[36mcached_file\u001b[39m\u001b[34m(path_or_repo_id, filename, **kwargs)\u001b[39m\n\u001b[32m 254\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcached_file\u001b[39m(\n\u001b[32m 255\u001b[39m path_or_repo_id: Union[\u001b[38;5;28mstr\u001b[39m, os.PathLike],\n\u001b[32m 256\u001b[39m filename: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m 257\u001b[39m **kwargs,\n\u001b[32m 258\u001b[39m ) -> Optional[\u001b[38;5;28mstr\u001b[39m]:\n\u001b[32m 259\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 260\u001b[39m \u001b[33;03m Tries to locate a file in a local folder and repo, downloads and cache it if necessary.\u001b[39;00m\n\u001b[32m 261\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 310\u001b[39m \u001b[33;03m ```\u001b[39;00m\n\u001b[32m 311\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m312\u001b[39m file = \u001b[43mcached_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_repo_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpath_or_repo_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilenames\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 313\u001b[39m file = file[\u001b[32m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m file\n\u001b[32m 314\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m file\n",
254
+ "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jeeva\\.conda\\envs\\textSummarizer\\Lib\\site-packages\\transformers\\utils\\hub.py:522\u001b[39m, in \u001b[36mcached_files\u001b[39m\u001b[34m(path_or_repo_id, filenames, 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[39m\n\u001b[32m 515\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[32m 516\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mPermissionError at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me.filename\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m when downloading \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_repo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 517\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mCheck cache directory permissions. Common causes: 1) another user is downloading the same model (please wait); \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 518\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m2) a previous download was canceled and the lock file needs manual removal.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 519\u001b[39m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n\u001b[32m 521\u001b[39m \u001b[38;5;66;03m# Now we try to recover if we can find all files correctly in the cache\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m522\u001b[39m resolved_files = \u001b[43m[\u001b[49m\n\u001b[32m 523\u001b[39m \u001b[43m \u001b[49m\u001b[43m_get_cache_file_to_return\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_repo_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfull_filenames\u001b[49m\n\u001b[32m 524\u001b[39m \u001b[43m\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 525\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m resolved_files):\n\u001b[32m 526\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m resolved_files\n",
255
+ "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jeeva\\.conda\\envs\\textSummarizer\\Lib\\site-packages\\transformers\\utils\\hub.py:523\u001b[39m, in \u001b[36m<listcomp>\u001b[39m\u001b[34m(.0)\u001b[39m\n\u001b[32m 515\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[32m 516\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mPermissionError at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me.filename\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m when downloading \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_repo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 517\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mCheck cache directory permissions. Common causes: 1) another user is downloading the same model (please wait); \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 518\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m2) a previous download was canceled and the lock file needs manual removal.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 519\u001b[39m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n\u001b[32m 521\u001b[39m \u001b[38;5;66;03m# Now we try to recover if we can find all files correctly in the cache\u001b[39;00m\n\u001b[32m 522\u001b[39m resolved_files = [\n\u001b[32m--> \u001b[39m\u001b[32m523\u001b[39m \u001b[43m_get_cache_file_to_return\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_repo_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m full_filenames\n\u001b[32m 524\u001b[39m ]\n\u001b[32m 525\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m resolved_files):\n\u001b[32m 526\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m resolved_files\n",
256
+ "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jeeva\\.conda\\envs\\textSummarizer\\Lib\\site-packages\\transformers\\utils\\hub.py:140\u001b[39m, in \u001b[36m_get_cache_file_to_return\u001b[39m\u001b[34m(path_or_repo_id, full_filename, cache_dir, revision)\u001b[39m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_cache_file_to_return\u001b[39m(\n\u001b[32m 137\u001b[39m path_or_repo_id: \u001b[38;5;28mstr\u001b[39m, full_filename: \u001b[38;5;28mstr\u001b[39m, cache_dir: Union[\u001b[38;5;28mstr\u001b[39m, Path, \u001b[38;5;28;01mNone\u001b[39;00m] = \u001b[38;5;28;01mNone\u001b[39;00m, revision: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 138\u001b[39m ):\n\u001b[32m 139\u001b[39m \u001b[38;5;66;03m# We try to see if we have a cached version (not up to date):\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m140\u001b[39m resolved_file = \u001b[43mtry_to_load_from_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_repo_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfull_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 141\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m resolved_file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m resolved_file != _CACHED_NO_EXIST:\n\u001b[32m 142\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m resolved_file\n",
257
+ "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jeeva\\.conda\\envs\\textSummarizer\\Lib\\site-packages\\huggingface_hub\\utils\\_validators.py:106\u001b[39m, in \u001b[36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 101\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m arg_name, arg_value \u001b[38;5;129;01min\u001b[39;00m chain(\n\u001b[32m 102\u001b[39m \u001b[38;5;28mzip\u001b[39m(signature.parameters, args), \u001b[38;5;66;03m# Args values\u001b[39;00m\n\u001b[32m 103\u001b[39m kwargs.items(), \u001b[38;5;66;03m# Kwargs values\u001b[39;00m\n\u001b[32m 104\u001b[39m ):\n\u001b[32m 105\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m arg_name \u001b[38;5;129;01min\u001b[39;00m [\u001b[33m\"\u001b[39m\u001b[33mrepo_id\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mfrom_id\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mto_id\u001b[39m\u001b[33m\"\u001b[39m]:\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m \u001b[43mvalidate_repo_id\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 108\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m arg_name == \u001b[33m\"\u001b[39m\u001b[33mtoken\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m arg_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 109\u001b[39m has_token = \u001b[38;5;28;01mTrue\u001b[39;00m\n",
258
+ "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jeeva\\.conda\\envs\\textSummarizer\\Lib\\site-packages\\huggingface_hub\\utils\\_validators.py:154\u001b[39m, in \u001b[36mvalidate_repo_id\u001b[39m\u001b[34m(repo_id)\u001b[39m\n\u001b[32m 151\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m HFValidationError(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mRepo id must be a string, not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(repo_id)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m: \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 153\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m repo_id.count(\u001b[33m\"\u001b[39m\u001b[33m/\u001b[39m\u001b[33m\"\u001b[39m) > \u001b[32m1\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m154\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m HFValidationError(\n\u001b[32m 155\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mRepo id must be in the form \u001b[39m\u001b[33m'\u001b[39m\u001b[33mrepo_name\u001b[39m\u001b[33m'\u001b[39m\u001b[33m or \u001b[39m\u001b[33m'\u001b[39m\u001b[33mnamespace/repo_name\u001b[39m\u001b[33m'\u001b[39m\u001b[33m:\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 156\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m. Use `repo_type` argument if needed.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 157\u001b[39m )\n\u001b[32m 159\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m REPO_ID_REGEX.match(repo_id):\n\u001b[32m 160\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m HFValidationError(\n\u001b[32m 161\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mRepo id must use alphanumeric chars or \u001b[39m\u001b[33m'\u001b[39m\u001b[33m-\u001b[39m\u001b[33m'\u001b[39m\u001b[33m, \u001b[39m\u001b[33m'\u001b[39m\u001b[33m_\u001b[39m\u001b[33m'\u001b[39m\u001b[33m, \u001b[39m\u001b[33m'\u001b[39m\u001b[33m.\u001b[39m\u001b[33m'\u001b[39m\u001b[33m, \u001b[39m\u001b[33m'\u001b[39m\u001b[33m--\u001b[39m\u001b[33m'\u001b[39m\u001b[33m and \u001b[39m\u001b[33m'\u001b[39m\u001b[33m..\u001b[39m\u001b[33m'\u001b[39m\u001b[33m are\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 162\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m forbidden, \u001b[39m\u001b[33m'\u001b[39m\u001b[33m-\u001b[39m\u001b[33m'\u001b[39m\u001b[33m and \u001b[39m\u001b[33m'\u001b[39m\u001b[33m.\u001b[39m\u001b[33m'\u001b[39m\u001b[33m cannot start or end the name, max length is 96:\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 163\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 164\u001b[39m )\n",
259
+ "\u001b[31mHFValidationError\u001b[39m: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'artifacts/model_trainer/tokenizer_name'. Use `repo_type` argument if needed."
260
+ ]
261
+ }
262
+ ],
263
+ "source": [
264
+ "try:\n",
265
+ " config = ConfigurationManager()\n",
266
+ " model_evaluation_config = config.get_model_evaluation_config()\n",
267
+ " model_evaluation_config = ModelEvaluation(config=model_evaluation_config)\n",
268
+ " model_evaluation_config.evaluate()\n",
269
+ "except Exception as e:\n",
270
+ " raise e"
271
  ]
272
  }
273
  ],
src/textSummarizer/components/model_trainer.py CHANGED
@@ -3,7 +3,8 @@ from transformers import DataCollatorForSeq2Seq
3
  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
4
  from datasets import load_dataset, load_from_disk
5
  from textSummarizer.entity import ModelTrainerConfig
6
- import torch
 
7
 
8
  class ModelTrainer:
9
  def __init__(self, config: ModelTrainerConfig):
 
3
  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
4
  from datasets import load_dataset, load_from_disk
5
  from textSummarizer.entity import ModelTrainerConfig
6
+ import torch
7
+ import os
8
 
9
  class ModelTrainer:
10
  def __init__(self, config: ModelTrainerConfig):