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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+book1.pdf filter=lfs diff=lfs merge=lfs -text

book1.pdf

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+oid sha256:b9f31116a8af081d0789518cdb8da191a1f5d3ca67988dd5f987781817458cf5
+size 3174678

emotional_buddy book2.ipynb

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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": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HiY1rH4fuPeF",
+        "outputId": "7c4cb24c-0a80-41fa-9f3e-7a79d7231921"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
+            "Collecting datasets\n",
+            "  Downloading datasets-3.1.0-py3-none-any.whl.metadata (20 kB)\n",
+            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.0+cu121)\n",
+            "Collecting faiss-cpu\n",
+            "  Downloading faiss_cpu-1.9.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)\n",
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+            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
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+            "Collecting xxhash (from datasets)\n",
+            "  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
+            "Collecting multiprocess<0.70.17 (from datasets)\n",
+            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
+            "Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\n",
+            "  Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n",
+            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.10.10)\n",
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+            "Downloading datasets-3.1.0-py3-none-any.whl (480 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+            "\u001b[?25hDownloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\n",
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+            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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+            "\u001b[?25hInstalling collected packages: xxhash, fsspec, faiss-cpu, dill, multiprocess, datasets\n",
+            "  Attempting uninstall: fsspec\n",
+            "    Found existing installation: fsspec 2024.10.0\n",
+            "    Uninstalling fsspec-2024.10.0:\n",
+            "      Successfully uninstalled fsspec-2024.10.0\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. 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 faiss-cpu-1.9.0 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install transformers datasets torch faiss-cpu\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "dataset = load_dataset(\"Amod/mental_health_counseling_conversations\")\n"
+      ],
+      "metadata": {
+        "id": "Zh2FSQW-uWkg"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Trainer, TrainingArguments\n",
+        "from datasets import load_dataset\n",
+        "# Instead of using train_test_split, we'll use Dataset.train_test_split\n",
+        "#from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "# Load dataset\n",
+        "dataset = load_dataset(\"Amod/mental_health_counseling_conversations\")\n",
+        "\n",
+        "# Split the dataset using Dataset.train_test_split\n",
+        "train_data = dataset['train'].train_test_split(test_size=0.2, seed=42)['train']  # 80% train\n",
+        "val_data = dataset['train'].train_test_split(test_size=0.2, seed=42)['test']  # 20% validation\n",
+        "\n",
+        "\n",
+        "# Load FLAN-T5 Small model and tokenizer\n",
+        "model_name = \"google/flan-t5-small\"\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+        "model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n",
+        "\n",
+        "# Preprocess the dataset\n",
+        "def preprocess_data(examples):\n",
+        "    # Tokenize the context and response as input-output pairs for the Seq2Seq model\n",
+        "    inputs = tokenizer(examples['Context'], padding=\"max_length\", truncation=True, max_length=512)\n",
+        "    targets = tokenizer(examples['Response'], padding=\"max_length\", truncation=True, max_length=128)\n",
+        "    inputs['labels'] = targets['input_ids']\n",
+        "    return inputs\n",
+        "\n",
+        "# Apply preprocessing to the train and validation datasets\n",
+        "train_data = train_data.map(preprocess_data, batched=True)\n",
+        "val_data = val_data.map(preprocess_data, batched=True)\n",
+        "\n",
+        "# Remove unnecessary columns (Context, Response) from the dataset after preprocessing\n",
+        "\n",
+        "\n",
+        "# Setup training arguments\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir='./results',\n",
+        "    evaluation_strategy=\"epoch\",\n",
+        "    learning_rate=2e-5,\n",
+        "    per_device_train_batch_size=2,\n",
+        "    per_device_eval_batch_size=2,\n",
+        "    num_train_epochs=5,\n",
+        "    logging_dir='./logs',\n",
+        "    logging_steps=10,\n",
+        "    save_strategy=\"epoch\",  # Changed save_strategy to 'epoch' to match evaluation_strategy\n",
+        "    save_steps=500,\n",
+        "    save_total_limit=2,\n",
+        "    load_best_model_at_end=True,\n",
+        "    metric_for_best_model='loss',\n",
+        ")\n",
+        "\n",
+        "# Initialize Trainer\n",
+        "trainer = Trainer(\n",
+        "    model=model,\n",
+        "    args=training_args,\n",
+        "    train_dataset=train_data,\n",
+        "    eval_dataset=val_data,\n",
+        "    tokenizer=tokenizer,\n",
+        ")\n",
+        "\n",
+        "# Train the model\n",
+        "trainer.train()\n",
+        "\n",
+        "# Save the fine-tuned model\n",
+        "trainer.save_model(\"fine_tuned_flan_t5\")\n",
+        "\n",
+        "# Now the model is fine-tuned and saved, we can use it for RAG"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "id": "uZ2imdKguqeb",
+        "outputId": "2ad6eefe-2d44-4e23-8456-ad294a750aba"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "metadata": {
+            "tags": null
+          },
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='6038' max='7025' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [6038/7025 15:08 < 02:28, 6.65 it/s, Epoch 4.30/5]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Epoch</th>\n",
+              "      <th>Training Loss</th>\n",
+              "      <th>Validation Loss</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>1</td>\n",
+              "      <td>3.433000</td>\n",
+              "      <td>3.051346</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>2</td>\n",
+              "      <td>3.104000</td>\n",
+              "      <td>2.949355</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>3</td>\n",
+              "      <td>3.121100</td>\n",
+              "      <td>2.920628</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>4</td>\n",
+              "      <td>3.126200</td>\n",
+              "      <td>2.906628</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='7025' max='7025' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [7025/7025 17:49, Epoch 5/5]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Epoch</th>\n",
+              "      <th>Training Loss</th>\n",
+              "      <th>Validation Loss</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>1</td>\n",
+              "      <td>3.433000</td>\n",
+              "      <td>3.051346</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>2</td>\n",
+              "      <td>3.104000</td>\n",
+              "      <td>2.949355</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>3</td>\n",
+              "      <td>3.121100</td>\n",
+              "      <td>2.920628</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>4</td>\n",
+              "      <td>3.126200</td>\n",
+              "      <td>2.906628</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>5</td>\n",
+              "      <td>3.051300</td>\n",
+              "      <td>2.902490</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "There were missing keys in the checkpoint model loaded: ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight'].\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainer.evaluate(train_data)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 124
+        },
+        "id": "sLp70Cgw7YTW",
+        "outputId": "7410e2b7-d4a9-459d-8e36-18d36b155493"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='1405' max='1405' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [1405/1405 01:05]\n",
+              "    </div>\n",
+              "    "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'eval_loss': 2.8036255836486816,\n",
+              " 'eval_runtime': 65.2915,\n",
+              " 'eval_samples_per_second': 43.022,\n",
+              " 'eval_steps_per_second': 21.519,\n",
+              " 'epoch': 5.0}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainer.evaluate(val_data)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 124
+        },
+        "id": "cSkT2mL77sVh",
+        "outputId": "7eba2cb2-6355-4686-c7cc-68a7cc657ad3"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='1757' max='1405' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [1405/1405 01:49]\n",
+              "    </div>\n",
+              "    "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'eval_loss': 2.9024901390075684,\n",
+              " 'eval_runtime': 23.9355,\n",
+              " 'eval_samples_per_second': 29.371,\n",
+              " 'eval_steps_per_second': 14.706,\n",
+              " 'epoch': 5.0}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
+        "\n",
+        "# Load the fine-tuned FLAN-T5 model and tokenizer\n",
+        "model_name = \"/content/results/checkpoint-5620\"  # Replace with the path to your fine-tuned model\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+        "model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n",
+        "\n",
+        "# Function to perform inference\n",
+        "def generate_response(input_text):\n",
+        "    # Encode the input text\n",
+        "    inputs = tokenizer(input_text, return_tensors=\"pt\", padding=True, truncation=True, max_length=512)\n",
+        "    # The model.generate function call should be indented inside the generate_response function\n",
+        "    output_ids = model.generate(\n",
+        "        inputs.input_ids,\n",
+        "        max_length=500,\n",
+        "        num_beams=4,\n",
+        "        temperature=0.7,\n",
+        "        top_p=0.9,\n",
+        "        top_k=50,\n",
+        "        do_sample=True,        # Sampling instead of beam search\n",
+        "        no_repeat_ngram_size=3, # Avoid repeating 3-grams\n",
+        "        length_penalty=1.0,    # Adjust this value based on your preference\n",
+        "        early_stopping=True\n",
+        "    )\n",
+        "    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
+        "    return response\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Example input text\n",
+        "input_text = \"I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here. I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it. How can I change my feeling of being worthless to everyone?\"\n",
+        "\n",
+        "# Perform inference\n",
+        "response = generate_response(input_text)\n",
+        "print(\"Response from the model:\", response)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dWM3cU22ypx6",
+        "outputId": "047e04f5-4720-4c1c-ac30-108368bbdd09"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Response from the model: I'm sorry to hear that you haven't tried or contemplated suicide. It sounds like you've been able to change your feeling of being worthless to everyone. It's a good idea to have a conversation with a mental health professional to discuss your feelings. If you're not sure what you want to do, you may want to talk to a psychiatrist about what you'd like to do. You may also want to ask a therapist if you are willing to help you with your feelings of worthlessness.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "sfrESf4s8dlP"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}