huggingface_transformers.ipynb

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   "source": [
    "# Hugging Face Transformers Example\n",
    "\n",
    "This is a simple example of how to use the Hugging Face Transformers library.\n",
    "\n",
    "More information can be found at https://huggingface.co/transformers/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fish/miniconda3/envs/huggingface/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).\n",
      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'label': 'POSITIVE', 'score': 0.9598051905632019}]\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "classifier = pipeline('sentiment-analysis')\n",
    "\n",
    "result = classifier(\"I've been waiting for a HuggingFace course my whole life.\")\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "config.json: 100%|██████████| 665/665 [00:00<00:00, 1.54MB/s]\n",
      "model.safetensors: 100%|██████████| 548M/548M [00:43<00:00, 12.5MB/s] \n",
      "generation_config.json: 100%|██████████| 124/124 [00:00<00:00, 352kB/s]\n",
      "vocab.json: 100%|██████████| 1.04M/1.04M [00:00<00:00, 1.34MB/s]\n",
      "merges.txt: 100%|██████████| 456k/456k [00:00<00:00, 931kB/s]\n",
      "tokenizer.json: 100%|██████████| 1.36M/1.36M [00:00<00:00, 1.53MB/s]\n",
      "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'generated_text': 'In this course, we will teach you how to build something to run, or run with, and how to control your environment, to control the power'}, {'generated_text': 'In this course, we will teach you how to find an optimal setting for your computer, how to configure it to handle the data streams that are necessary'}]\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "generator = pipeline(\"text-generation\", model=\"gpt2\")  # you can use any model from the model hub\n",
    "\n",
    "result = generator(\n",
    "    \"In this course, we will teach you how to\",\n",
    "    max_length=30,\n",
    "    num_return_sequences=2,\n",
    ")\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zero-shot Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to facebook/bart-large-mnli and revision c626438 (https://huggingface.co/facebook/bart-large-mnli).\n",
      "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
      "config.json: 100%|██████████| 1.15k/1.15k [00:00<00:00, 3.09MB/s]\n",
      "model.safetensors: 100%|██████████| 1.63G/1.63G [02:09<00:00, 12.6MB/s]\n",
      "tokenizer_config.json: 100%|██████████| 26.0/26.0 [00:00<00:00, 49.8kB/s]\n",
      "vocab.json: 100%|██████████| 899k/899k [00:00<00:00, 1.17MB/s]\n",
      "merges.txt: 100%|██████████| 456k/456k [00:00<00:00, 817kB/s]\n",
      "tokenizer.json: 100%|██████████| 1.36M/1.36M [00:00<00:00, 1.43MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sequence': 'This is a course about the Transformers library', 'labels': ['education', 'business', 'politics'], 'scores': [0.8445983529090881, 0.11197486519813538, 0.04342678561806679]}\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "classifier = pipeline('zero-shot-classification')\n",
    "\n",
    "result = classifier(\n",
    "    \"This is a course about the Transformers library\",\n",
    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
    ")\n",
    "\n",
    "print(result)"
   ]
  }
 ],
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