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This behaviour is the source of the following dependency conflicts.\n","gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed datasets-3.1.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"]}],"source":["pip install transformers datasets accelerate"]},{"cell_type":"code","source":["!pip install datasets"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H9d4TyaVya8D","executionInfo":{"status":"ok","timestamp":1733382026740,"user_tz":-300,"elapsed":4691,"user":{"displayName":"qusai aamir","userId":"15867633959240373143"}},"outputId":"bf832afa-8e27-4313-c2ea-5bfee3745ac4"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting datasets\n"," Downloading datasets-3.1.0-py3-none-any.whl.metadata (20 kB)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n","Requirement 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This behaviour is the source of the following dependency conflicts.\n","gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed datasets-3.1.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"]}]},{"cell_type":"code","source":["from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForSeq2Seq\n","from datasets import load_dataset\n","\n","# Load GPT-Neo tokenizer and model\n","tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/gpt-neo-125M\")\n","model = AutoModelForCausalLM.from_pretrained(\"EleutherAI/gpt-neo-125M\")\n","\n","# Set pad token to eos token\n","tokenizer.pad_token = tokenizer.eos_token\n","\n","# Load the subset dataset\n","dataset = load_dataset(\"json\", data_files=\"financial_accounting_subset.json\")\n","\n","# Inspect the first row to verify the structure of the dataset\n","first_row = next(iter(dataset[\"train\"]))\n","print(\"Keys in the dataset:\", first_row.keys())\n","\n","# Define input and output columns based on your dataset structure\n","# Adjust these as necessary for your task\n","input_column = \"Description\" # Example input: financial transaction description\n","output_column = \"Category\" # Example output: transaction category\n","\n","# Check if the specified columns exist in the dataset\n","if input_column not in first_row or output_column not in first_row:\n"," raise KeyError(\n"," f\"Columns '{input_column}' or '{output_column}' not found in dataset. \"\n"," f\"Available keys: {list(first_row.keys())}\"\n"," )\n","\n","# Define the tokenization function\n","def tokenize_function(examples):\n"," inputs = examples[input_column]\n"," targets = examples[output_column]\n"," model_inputs = tokenizer(\n"," inputs,\n"," truncation=True,\n"," padding=\"max_length\",\n"," max_length=512\n"," )\n"," with tokenizer.as_target_tokenizer():\n"," labels = tokenizer(\n"," targets,\n"," truncation=True,\n"," padding=\"max_length\",\n"," max_length=512\n"," )\n"," model_inputs[\"labels\"] = labels[\"input_ids\"]\n"," return model_inputs\n","\n","# Tokenize the dataset\n","tokenized_dataset = dataset.map(tokenize_function, batched=True)\n","\n","# Optional: Save the tokenized dataset for inspection\n","tokenized_dataset.save_to_disk(\"./tokenized_dataset\")\n","\n","print(\"Tokenization complete!\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":174,"referenced_widgets":["a84764be72c94e9b92b4bfa1c39f5a5d","203be9dfa34d44c48cf81c407af93e7f","23c261bb2a7643d0b157435d23bfa579","e1ab3c1fd28b41baa1b03c41263c5e8a","f0730089d3c148d7930d64d1fb32baf3","7d732638e92f4003a188de33b11728d9","3c47de8fb43445dd8da87c0c92b66a41","98ad2005ca124300be0bc8a164a9c47d","bb57322fe0a444bc8a6a4d763b5a048f","4476cc1ba6604a17b1edf9f02b61e961","fe699bfd6b824a7d93986846e6300206","c8015686d7b64cd8b538f41e10126dfc","eafdd13a06d8465687dc3f6313064fb5","c0ec5bdd73b5488fa6804b5d97d12899","444cf550822e42399f2bdd4634c4acf5","ac8428a7457f4c37b0328bb8131c6b31","8c78493333f442ecb93b5b7ee5b80f76","5506934b176b41eea436c10ca6e95aaa","d9b238b007c04e7db7539d05cd384ff2","85d38d2774f84bdeaea6468d5306a709","6b22798cff1f493297a2c02882d95c23","b2bbb20b70c547388993e2464250c48f"]},"id":"h3DHVIvlySz0","executionInfo":{"status":"ok","timestamp":1733382302221,"user_tz":-300,"elapsed":32633,"user":{"displayName":"qusai aamir","userId":"15867633959240373143"}},"outputId":"ae8c24fb-c29b-43cb-9e62-5f352ff3b60d"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Keys in the dataset: dict_keys(['Date', 'Account', 'Description', 'Debit', 'Credit', 'Category', 'Transaction_Type', 'Customer_Vendor', 'Payment_Method', 'Reference'])\n"]},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/50001 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a84764be72c94e9b92b4bfa1c39f5a5d"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:4114: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Saving the dataset (0/1 shards): 0%| | 0/50001 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c8015686d7b64cd8b538f41e10126dfc"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Tokenization complete!\n"]}]},{"cell_type":"code","source":["# Define a custom data collator to handle loss computation\n","class CustomDataCollator(DataCollatorForSeq2Seq):\n"," def __call__(self, features):\n"," # Ensure correct padding and label creation\n"," batch = super().__call__(features)\n"," labels = batch['input_ids'].clone()\n"," labels[labels == tokenizer.pad_token_id] = -100 # Ignore padding tokens in the loss computation\n"," batch['labels'] = labels\n"," return batch\n","\n","# Define the training arguments\n","training_args = TrainingArguments(\n"," output_dir=\"./gpt_fine_tuned_model\",\n"," num_train_epochs=1,\n"," per_device_train_batch_size=8,\n"," per_device_eval_batch_size=8,\n"," logging_dir='./logs',\n"," logging_steps=500,\n"," do_train=True,\n"," do_eval=True,\n"," report_to=\"none\", # Change to \"wandb\" if using Weights & Biases\n",")\n","\n","# Initialize the Trainer with the custom data collator\n","trainer = Trainer(\n"," model=model,\n"," args=training_args,\n"," train_dataset=tokenized_dataset[\"train\"], # Use the tokenized training dataset\n"," eval_dataset=tokenized_dataset[\"train\"], # Optional: if you have a validation set\n"," data_collator=CustomDataCollator(tokenizer), # Use custom data collator\n",")\n","\n","# Start training\n","trainer.train()\n","\n","# Save the fine-tuned model\n","trainer.save_model(\"./gpt_fine_tuned_model\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":854},"id":"AGnSEH4KUSqA","executionInfo":{"status":"ok","timestamp":1733389506675,"user_tz":-300,"elapsed":808345,"user":{"displayName":"qusai aamir","userId":"15867633959240373143"}},"outputId":"4bde9e00-89d8-457d-9824-4d67239aec04"},"execution_count":6,"outputs":[{"data":{"text/html":["\n"," <div>\n"," \n"," <progress value='5522' max='6251' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [5522/6251 1:43:22 < 13:39, 0.89 it/s, Epoch 0.88/1]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>500</td>\n"," <td>5.629600</td>\n"," </tr>\n"," <tr>\n"," <td>1000</td>\n"," <td>5.499100</td>\n"," </tr>\n"," <tr>\n"," <td>1500</td>\n"," <td>5.465100</td>\n"," </tr>\n"," <tr>\n"," <td>2000</td>\n"," <td>5.445500</td>\n"," </tr>\n"," <tr>\n"," <td>2500</td>\n"," <td>5.419400</td>\n"," </tr>\n"," <tr>\n"," <td>3000</td>\n"," <td>5.433800</td>\n"," </tr>\n"," <tr>\n"," <td>3500</td>\n"," <td>5.417200</td>\n"," </tr>\n"," <tr>\n"," <td>4000</td>\n"," <td>5.385300</td>\n"," </tr>\n"," <tr>\n"," <td>4500</td>\n"," <td>5.385200</td>\n"," </tr>\n"," <tr>\n"," <td>5000</td>\n"," <td>5.386900</td>\n"," </tr>\n"," <tr>\n"," <td>5500</td>\n"," <td>5.388800</td>\n"," </tr>\n"," </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='6251' max='6251' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [6251/6251 1:56:47, Epoch 1/1]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>500</td>\n"," <td>5.629600</td>\n"," </tr>\n"," <tr>\n"," <td>1000</td>\n"," <td>5.499100</td>\n"," </tr>\n"," <tr>\n"," <td>1500</td>\n"," <td>5.465100</td>\n"," </tr>\n"," <tr>\n"," <td>2000</td>\n"," <td>5.445500</td>\n"," </tr>\n"," <tr>\n"," <td>2500</td>\n"," <td>5.419400</td>\n"," </tr>\n"," <tr>\n"," <td>3000</td>\n"," <td>5.433800</td>\n"," </tr>\n"," <tr>\n"," <td>3500</td>\n"," <td>5.417200</td>\n"," </tr>\n"," <tr>\n"," <td>4000</td>\n"," <td>5.385300</td>\n"," </tr>\n"," <tr>\n"," <td>4500</td>\n"," <td>5.385200</td>\n"," </tr>\n"," <tr>\n"," <td>5000</td>\n"," <td>5.386900</td>\n"," </tr>\n"," <tr>\n"," <td>5500</td>\n"," <td>5.388800</td>\n"," </tr>\n"," <tr>\n"," <td>6000</td>\n"," <td>5.345600</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}}]},{"cell_type":"code","source":["from transformers import AutoTokenizer\n","\n","# Save model and tokenizer\n","model.save_pretrained(\"path_to_save_model\")\n","tokenizer.save_pretrained(\"path_to_save_model\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6h7qjNWfdzvI","executionInfo":{"status":"ok","timestamp":1733389816795,"user_tz":-300,"elapsed":3653,"user":{"displayName":"qusai aamir","userId":"15867633959240373143"}},"outputId":"da787b7c-fafa-41cb-a3b9-5609496ca751"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["('path_to_save_model/tokenizer_config.json',\n"," 'path_to_save_model/special_tokens_map.json',\n"," 'path_to_save_model/vocab.json',\n"," 'path_to_save_model/merges.txt',\n"," 'path_to_save_model/added_tokens.json',\n"," 'path_to_save_model/tokenizer.json')"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["results = trainer.evaluate()\n","print(\"Evaluation Results:\", results)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":37},"id":"FmYpQ5iqFcd9","outputId":"51406d29-1ffa-4d54-a6da-8f189197da67"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='1370' max='6251' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [1370/6251 08:41 < 30:58, 2.63 it/s]\n"," </div>\n"," "]},"metadata":{}}]},{"cell_type":"code","source":["test_inputs = [\n"," \"Describe a transaction for a purchase at a retail store.\", # Example prompt\n"," \"Explain a bank deposit transaction.\", # Another example\n","]\n","\n","for input_text in test_inputs:\n"," inputs = tokenizer(input_text, return_tensors=\"pt\", padding=True, truncation=True)\n"," outputs = model.generate(**inputs, max_length=50)\n"," generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n"," print(f\"Input: {input_text}\")\n"," print(f\"Generated Output: {generated_text}\")"],"metadata":{"id":"jumxol5dFd3V"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from huggingface_hub import HfApi\n","\n","api = HfApi()\n","api.upload_folder(\n"," folder_path=\"path_to_save_model\", # Path where the model is saved\n"," repo_id=\"your_username/your_model_name\",\n"," repo_type=\"model\"\n",")\n"],"metadata":{"id":"Dcd9-URpHPGz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pip install fastapi uvicorn"],"metadata":{"id":"5UIdeEXJFukP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from fastapi import FastAPI, Request\n","from transformers import AutoModelForCausalLM, AutoTokenizer\n","\n","app = FastAPI()\n","\n","model = AutoModelForCausalLM.from_pretrained(\"path_to_save_model\")\n","tokenizer = AutoTokenizer.from_pretrained(\"path_to_save_model\")\n","\n","@app.post(\"/generate\")\n","async def generate(request: Request):\n"," data = await request.json()\n"," input_text = data.get(\"input\")\n"," inputs = tokenizer(input_text, return_tensors=\"pt\", padding=True, truncation=True)\n"," outputs = model.generate(**inputs, max_length=50)\n"," return {\"output\": tokenizer.decode(outputs[0], skip_special_tokens=True)}\n","\n","# Run using uvicorn\n","# uvicorn your_script_name:app --reload"],"metadata":{"id":"YcQ1ublCF4VU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pip install streamlit"],"metadata":{"id":"MdqG-CMNF83L"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import streamlit as st\n","from transformers import AutoModelForCausalLM, AutoTokenizer\n","\n","model = AutoModelForCausalLM.from_pretrained(\"path_to_save_model\")\n","tokenizer = AutoTokenizer.from_pretrained(\"path_to_save_model\")\n","\n","st.title(\"Accounting GPT\")\n","input_text = st.text_input(\"Enter a prompt:\")\n","if st.button(\"Generate\"):\n"," inputs = tokenizer(input_text, return_tensors=\"pt\", padding=True, truncation=True)\n"," outputs = model.generate(**inputs, max_length=50)\n"," st.write(tokenizer.decode(outputs[0], skip_special_tokens=True))\n"],"metadata":{"id":"-cmJJzbkGD0H"},"execution_count":null,"outputs":[]}]}
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