machine-learning/nlp/chatbot-transformers/DialoGPT.ipynb

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        "model_name = \"microsoft/DialoGPT-medium\"\n",
        "# model_name = \"microsoft/DialoGPT-small\"\n",
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          "base_uri": "https://localhost:8080/"
        },
        "id": "hLlcHnisQYeU",
        "outputId": "a02694cf-a1f8-4027-a371-259f5770c43d"
      },
      "source": [
        "# chatting 5 times with greedy search\n",
        "for step in range(5):\n",
        "    # take user input\n",
        "    text = input(\">> You:\")\n",
        "    # encode the input and add end of string token\n",
        "    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n",
        "    # concatenate new user input with chat history (if there is)\n",
        "    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n",
        "    # generate a bot response\n",
        "    chat_history_ids = model.generate(\n",
        "        bot_input_ids,\n",
        "        max_length=1000,\n",
        "        pad_token_id=tokenizer.eos_token_id,\n",
        "    )\n",
        "    #print the output\n",
        "    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n",
        "    print(f\"DialoGPT: {output}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4BowrvR6QY7V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97b24dd0-c4a0-4499-c149-f0263d6ed5e7"
      },
      "source": [
        "# chatting 5 times with beam search\n",
        "for step in range(5):\n",
        "    # take user input\n",
        "    text = input(\">> You:\")\n",
        "    # encode the input and add end of string token\n",
        "    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n",
        "    # concatenate new user input with chat history (if there is)\n",
        "    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n",
        "    # generate a bot response\n",
        "    chat_history_ids = model.generate(\n",
        "        bot_input_ids,\n",
        "        max_length=1000,\n",
        "        num_beams=3,\n",
        "        early_stopping=True,\n",
        "        pad_token_id=tokenizer.eos_token_id\n",
        "    )\n",
        "    #print the output\n",
        "    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n",
        "    print(f\"DialoGPT: {output}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fl96NOmzGKDl",
        "outputId": "23992d2c-413d-4fcd-afc3-24e47170c4b2"
      },
      "source": [
        "# chatting 5 times with sampling\n",
        "for step in range(5):\n",
        "    # take user input\n",
        "    text = input(\">> You:\")\n",
        "    # encode the input and add end of string token\n",
        "    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n",
        "    # concatenate new user input with chat history (if there is)\n",
        "    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n",
        "    # generate a bot response\n",
        "    chat_history_ids = model.generate(\n",
        "        bot_input_ids,\n",
        "        max_length=1000,\n",
        "        do_sample=True,\n",
        "        top_k=0,\n",
        "        pad_token_id=tokenizer.eos_token_id\n",
        "    )\n",
        "    #print the output\n",
        "    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n",
        "    print(f\"DialoGPT: {output}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "kxooQQiVJXVd",
        "outputId": "1c81b56c-5eb1-4263-f763-9ddee7d9897b"
      },
      "source": [
        "# chatting 5 times with sampling & tweaking temperature\n",
        "for step in range(5):\n",
        "    # take user input\n",
        "    text = input(\">> You:\")\n",
        "    # encode the input and add end of string token\n",
        "    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n",
        "    # concatenate new user input with chat history (if there is)\n",
        "    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n",
        "    # generate a bot response\n",
        "    chat_history_ids = model.generate(\n",
        "        bot_input_ids,\n",
        "        max_length=1000,\n",
        "        do_sample=True,\n",
        "        top_k=0,\n",
        "        temperature=0.75,\n",
        "        pad_token_id=tokenizer.eos_token_id\n",
        "    )\n",
        "    #print the output\n",
        "    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n",
        "    print(f\"DialoGPT: {output}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v0K9mYk_KMsI",
        "outputId": "3c627eeb-0c64-45fe-9533-884865a24665"
      },
      "source": [
        "# chatting 5 times with Top K sampling & tweaking temperature\n",
        "for step in range(5):\n",
        "    # take user input\n",
        "    text = input(\">> You:\")\n",
        "    # encode the input and add end of string token\n",
        "    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n",
        "    # concatenate new user input with chat history (if there is)\n",
        "    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n",
        "    # generate a bot response\n",
        "    chat_history_ids = model.generate(\n",
        "        bot_input_ids,\n",
        "        max_length=1000,\n",
        "        do_sample=True,\n",
        "        top_k=100,\n",
        "        temperature=0.75,\n",
        "        pad_token_id=tokenizer.eos_token_id\n",
        "    )\n",
        "    #print the output\n",
        "    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n",
        "    print(f\"DialoGPT: {output}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lbj7DOfKKuct",
        "outputId": "c87b97ff-b0d6-4d8a-eeae-419f875aa66b"
      },
      "source": [
        "# chatting 5 times with nucleus sampling & tweaking temperature\n",
        "for step in range(5):\n",
        "    # take user input\n",
        "    text = input(\">> You:\")\n",
        "    # encode the input and add end of string token\n",
        "    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n",
        "    # concatenate new user input with chat history (if there is)\n",
        "    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n",
        "    # generate a bot response\n",
        "    chat_history_ids = model.generate(\n",
        "        bot_input_ids,\n",
        "        max_length=1000,\n",
        "        do_sample=True,\n",
        "        top_p=0.95,\n",
        "        top_k=0,\n",
        "        temperature=0.75,\n",
        "        pad_token_id=tokenizer.eos_token_id\n",
        "    )\n",
        "    #print the output\n",
        "    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n",
        "    print(f\"DialoGPT: {output}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eIAojx7aNndK",
        "outputId": "2abca6c7-1bcc-4d45-95e1-04a9790fec55"
      },
      "source": [
        "# chatting 5 times with nucleus & top-k sampling & tweaking temperature & multiple\n",
        "# sentences\n",
        "for step in range(5):\n",
        "    # take user input\n",
        "    text = input(\">> You:\")\n",
        "    # encode the input and add end of string token\n",
        "    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n",
        "    # concatenate new user input with chat history (if there is)\n",
        "    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n",
        "    # generate a bot response\n",
        "    chat_history_ids_list = model.generate(\n",
        "        bot_input_ids,\n",
        "        max_length=1000,\n",
        "        do_sample=True,\n",
        "        top_p=0.95,\n",
        "        top_k=50,Y\n",
        "        temperature=0.75,\n",
        "        num_return_sequences=5,\n",
        "        pad_token_id=tokenizer.eos_token_id\n",
        "    )\n",
        "    #print the outputs\n",
        "    for i in range(len(chat_history_ids_list)):\n",
        "      output = tokenizer.decode(chat_history_ids_list[i][bot_input_ids.shape[-1]:], skip_special_tokens=True)\n",
        "      print(f\"DialoGPT {i}: {output}\")\n",
        "    choice_index = int(input(\"Choose the response you want for the next input: \"))\n",
        "    chat_history_ids = torch.unsqueeze(chat_history_ids_list[choice_index], dim=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77vpEd8jQ7W8"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}