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"start_time": "2024-07-01T11:08:00.067738Z"
},
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},
"source": [
"import torch\n",
"from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
],
"outputs": [],
"execution_count": 2
},
{
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"source": [
"tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-xl\")\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(\"google/flan-t5-xl\")"
],
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{
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"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
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"T5ForConditionalGeneration(\n",
" (shared): Embedding(32128, 2048)\n",
" (encoder): T5Stack(\n",
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" (block): ModuleList(\n",
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" (q): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (k): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (v): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (o): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (relative_attention_bias): Embedding(32, 32)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): T5LayerFF(\n",
" (DenseReluDense): T5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=2048, out_features=5120, bias=False)\n",
" (wi_1): Linear(in_features=2048, out_features=5120, bias=False)\n",
" (wo): Linear(in_features=5120, out_features=2048, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (1-23): 23 x T5Block(\n",
" (layer): ModuleList(\n",
" (0): T5LayerSelfAttention(\n",
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" (v): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (o): Linear(in_features=2048, out_features=2048, bias=False)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): T5LayerFF(\n",
" (DenseReluDense): T5DenseGatedActDense(\n",
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" (wi_1): Linear(in_features=2048, out_features=5120, bias=False)\n",
" (wo): Linear(in_features=5120, out_features=2048, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
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" )\n",
" )\n",
" )\n",
" )\n",
" (final_layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (decoder): T5Stack(\n",
" (embed_tokens): Embedding(32128, 2048)\n",
" (block): ModuleList(\n",
" (0): T5Block(\n",
" (layer): ModuleList(\n",
" (0): T5LayerSelfAttention(\n",
" (SelfAttention): T5Attention(\n",
" (q): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (k): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (v): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (o): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (relative_attention_bias): Embedding(32, 32)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): T5LayerCrossAttention(\n",
" (EncDecAttention): T5Attention(\n",
" (q): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (k): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (v): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (o): Linear(in_features=2048, out_features=2048, bias=False)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (2): T5LayerFF(\n",
" (DenseReluDense): T5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=2048, out_features=5120, bias=False)\n",
" (wi_1): Linear(in_features=2048, out_features=5120, bias=False)\n",
" (wo): Linear(in_features=5120, out_features=2048, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (1-23): 23 x T5Block(\n",
" (layer): ModuleList(\n",
" (0): T5LayerSelfAttention(\n",
" (SelfAttention): T5Attention(\n",
" (q): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (k): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (v): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (o): Linear(in_features=2048, out_features=2048, bias=False)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): T5LayerCrossAttention(\n",
" (EncDecAttention): T5Attention(\n",
" (q): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (k): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (v): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (o): Linear(in_features=2048, out_features=2048, bias=False)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (2): T5LayerFF(\n",
" (DenseReluDense): T5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=2048, out_features=5120, bias=False)\n",
" (wi_1): Linear(in_features=2048, out_features=5120, bias=False)\n",
" (wo): Linear(in_features=5120, out_features=2048, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (final_layer_norm): T5LayerNorm()\n",
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" )\n",
" (lm_head): Linear(in_features=2048, out_features=32128, bias=False)\n",
")"
]
},
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]
},
{
"cell_type": "code",
"source": [
"from transformers import Trainer, TrainingArguments\n",
"\n",
"# Define training arguments\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./output\",\n",
" num_train_epochs=3,\n",
" per_device_train_batch_size=8,\n",
" # ... other training arguments\n",
")\n",
"\n",
"# Create a Trainer object\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=Financial.csv, # Load your training dataset\n",
")\n",
"\n",
"# Start training\n",
"trainer.train()"
],
"metadata": {
"id": "j_Y33_fMfGd9"
},
"id": "j_Y33_fMfGd9",
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"id": "7ce8ee88e61ac738",
"metadata": {
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"end_time": "2024-07-01T11:08:50.346303Z",
"start_time": "2024-07-01T11:08:50.336252Z"
},
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},
"source": [
"def get_response(prompt, max_new_tokens=50):\n",
" inputs = tokenizer(prompt, return_tensors=\"pt\").to(device)\n",
" outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, temperature= 0.0001, do_sample=True)\n",
" response = tokenizer.decode(outputs[0], skip_special_tokens=True) # Use indexing instead of calling\n",
" return response"
],
"outputs": [],
"execution_count": 15
},
{
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"prompt ='What is capital of Madhya Pradesh'\n",
"get_response(prompt, max_new_tokens=50)"
],
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{
"output_type": "execute_result",
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"'bhopal'"
],
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