Add transformer use cases

training.ipynb

@@ -0,0 +1,945 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Transformer Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import transformers"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transformers, what can they do?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'label': 'POSITIVE', 'score': 0.6012226343154907}]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "classifier = pipeline(\"sentiment-analysis\")\n",
+    "classifier(\"OMG this is my first time trying this!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'label': 'POSITIVE', 'score': 0.9998352527618408},\n",
+       " {'label': 'NEGATIVE', 'score': 0.9995977282524109}]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classifier(\n",
+    "    [\"I really like this a lot!\", \"I hate it like this.\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'sequence': 'How to differentiate sun and cloud?',\n",
+       " 'labels': ['education', 'business', 'politics'],\n",
+       " 'scores': [0.7144545316696167, 0.19746531546115875, 0.08808010816574097]}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classifier = pipeline(\"zero-shot-classification\")\n",
+    "classifier(\n",
+    "    \"How to differentiate sun and cloud?\",\n",
+    "    candidate_labels = [\"education\", \"politics\", \"business\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No model was supplied, defaulted to gpt2 and revision 6c0e608 (https://huggingface.co/gpt2).\n",
+      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f065452c7f924df7a5666b71186fd6d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f261021fbdc5427abc2c54914de96ed2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3dbab5273b64fb09e75ced6c5380c1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "85df61ca152e4e9dabccb090df5b195e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a371f13013f4e90890b17b0d56fa7d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "389ab29d60d64fd7bc9c7745599cf713",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
+      "/Users/florentiana.yuwono/anaconda3/lib/python3.10/site-packages/transformers/generation/utils.py:1353: UserWarning: Using `max_length`'s default (50) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'generated_text': \"In this class, I will speak about something I've been thinking about for quite some time and it won't even come up for a while.\\n\\nLet's be honest and tell you; it has to be so simple. You do not need\"}]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "generator = pipeline(\"text-generation\")\n",
+    "generator(\"In this class, I will speak\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d893dfa6d45e4dce8f8f548ce903c330",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e0cafc878e94597872168c19bcfbe00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a374402e1a4748f4b3b8b964589f6868",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8476884d2c114598904427a1891a6beb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4730d96e505a4cf0b9f2761a1201822b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca8e61625c024428963339dfca8ecb67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'generated_text': 'In this class, I will speak as a lecturer to the students on social media (for those of you who are interested).\\nThere are many classes'},\n",
+       " {'generated_text': 'In this class, I will speak for a particular type of group of writers that I want to talk about, like us writers like us writers, people'}]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
+    "generator(\n",
+    "    \"In this class, I will speak\",\n",
+    "    max_length=30,\n",
+    "    num_return_sequences=2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No model was supplied, defaulted to distilroberta-base and revision ec58a5b (https://huggingface.co/distilroberta-base).\n",
+      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf08df8aa5a444649e21d7c0d7e64039",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b5e4348c7904d0890da6b768461c5af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "100d8fbb75b14bea92bb635b116c1d08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4cdc5db6d7541918624369df29642ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d808f036abef4c62bea2dd62601502e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'score': 0.06216174364089966,\n",
+       "  'token': 42,\n",
+       "  'token_str': ' this',\n",
+       "  'sequence': 'The sky is blue and bright, I wonder what this is about.'},\n",
+       " {'score': 0.040428631007671356,\n",
+       "  'token': 24,\n",
+       "  'token_str': ' it',\n",
+       "  'sequence': 'The sky is blue and bright, I wonder what it is about.'},\n",
+       " {'score': 0.023530298843979836,\n",
+       "  'token': 14,\n",
+       "  'token_str': ' that',\n",
+       "  'sequence': 'The sky is blue and bright, I wonder what that is about.'}]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unmasker = pipeline(\"fill-mask\")\n",
+    "unmasker(\"The sky is blue and bright, I wonder what <mask> is about.\", top_k=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english).\n",
+      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1a918d701ee46ccb54ab2989305585f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/998 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a95eff2616b64dcfba172bc43dfe23be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/1.33G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63c9f30a9542417aabc57971b9d9b44f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e61894113e164c1eb5a66ad300971bfe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/213k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/florentiana.yuwono/anaconda3/lib/python3.10/site-packages/transformers/pipelines/token_classification.py:169: UserWarning: `grouped_entities` is deprecated and will be removed in version v5.0.0, defaulted to `aggregation_strategy=\"simple\"` instead.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'entity_group': 'LOC',\n",
+       "  'score': 0.86960346,\n",
+       "  'word': 'Owl City',\n",
+       "  'start': 56,\n",
+       "  'end': 64}]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ner = pipeline(\"ner\", grouped_entities=True)\n",
+    "\n",
+    "ner(\"Mine is Hilarious, usually spotted at united nations in Owl City.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No model was supplied, defaulted to distilbert-base-cased-distilled-squad and revision 626af31 (https://huggingface.co/distilbert-base-cased-distilled-squad).\n",
+      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95136a50322745ea8715e36e9e2a4b50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/473 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1440abd06884310b97a38e669641a21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+    },
+    {
+     "data": {
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+       "version_minor": 0
+      },
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7160d2a6f8a942d7b298453684e9ee49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'score': 0.4208132028579712,\n",
+       " 'start': 0,\n",
+       " 'end': 44,\n",
+       " 'answer': 'Mine is Hilarious, usually grab from library'}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qa = pipeline(\"question-answering\")\n",
+    "qa(\n",
+    "    question=\"Where is it?\",\n",
+    "    context=\"Mine is Hilarious, usually grab from library though.\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 and revision a4f8f3e (https://huggingface.co/sshleifer/distilbart-cnn-12-6).\n",
+      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "201842ea28eb45b9838d9ad77e90b528",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/1.80k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "848fa320643340798fc4e3c6119b567e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/1.22G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf8110edeb18471fa048308bb782b089",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b05eb6e6ada44e1ea88342739fc0f215",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/899k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "992d67bdfdb64d2387f4029d4edb8d4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'summary_text': ' America has changed dramatically during recent years . The number of engineering graduates in the U.S. has declined in traditional engineering disciplines such as mechanical, civil, electrical, chemical, and aeronautical engineering . Rapidly developing economies such as China and India, as well as other industrial countries in Europe and Asia, continue to encourage and advance engineering .'}]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summarizer = pipeline(\"summarization\")\n",
+    "summarizer(\n",
+    "    \"\"\"\n",
+    "    America has changed dramatically during recent years. Not only has the number of \n",
+    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
+    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
+    "    the premier American universities engineering curricula now concentrate on \n",
+    "    and encourage largely the study of engineering science. As a result, there \n",
+    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
+    "    the environment, and related issues, and greater concentration on high \n",
+    "    technology subjects, largely supporting increasingly complex scientific \n",
+    "    developments. While the latter is important, it should not be at the expense \n",
+    "    of more traditional engineering.\n",
+    "\n",
+    "    Rapidly developing economies such as China and India, as well as other \n",
+    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
+    "    the teaching of engineering. Both China and India, respectively, graduate \n",
+    "    six and eight times as many traditional engineers as does the United States. \n",
+    "    Other industrial countries at minimum maintain their output, while America \n",
+    "    suffers an increasingly serious decline in the number of engineering graduates \n",
+    "    and a lack of well-educated engineers.\n",
+    "    \"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "This tokenizer cannot be instantiated. Please make sure you have `sentencepiece` installed in order to use this tokenizer.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[24], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m translator \u001b[39m=\u001b[39m pipeline(\u001b[39m\"\u001b[39;49m\u001b[39mtranslation\u001b[39;49m\u001b[39m\"\u001b[39;49m, model\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mHelsinki-NLP/opus-mt-fr-en\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m      2\u001b[0m translator(\u001b[39m\"\u001b[39m\u001b[39mCe cours est produit par.\u001b[39m\u001b[39m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/transformers/pipelines/__init__.py:885\u001b[0m, in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, use_auth_token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m    882\u001b[0m             tokenizer_kwargs \u001b[39m=\u001b[39m model_kwargs\u001b[39m.\u001b[39mcopy()\n\u001b[1;32m    883\u001b[0m             tokenizer_kwargs\u001b[39m.\u001b[39mpop(\u001b[39m\"\u001b[39m\u001b[39mtorch_dtype\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m--> 885\u001b[0m         tokenizer \u001b[39m=\u001b[39m AutoTokenizer\u001b[39m.\u001b[39;49mfrom_pretrained(\n\u001b[1;32m    886\u001b[0m             tokenizer_identifier, use_fast\u001b[39m=\u001b[39;49muse_fast, _from_pipeline\u001b[39m=\u001b[39;49mtask, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mhub_kwargs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mtokenizer_kwargs\n\u001b[1;32m    887\u001b[0m         )\n\u001b[1;32m    889\u001b[0m \u001b[39mif\u001b[39;00m load_image_processor:\n\u001b[1;32m    890\u001b[0m     \u001b[39m# Try to infer image processor from model or config name (if provided as str)\u001b[39;00m\n\u001b[1;32m    891\u001b[0m     \u001b[39mif\u001b[39;00m image_processor \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py:714\u001b[0m, in \u001b[0;36mAutoTokenizer.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    712\u001b[0m             \u001b[39mreturn\u001b[39;00m tokenizer_class_py\u001b[39m.\u001b[39mfrom_pretrained(pretrained_model_name_or_path, \u001b[39m*\u001b[39minputs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    713\u001b[0m         \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 714\u001b[0m             \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    715\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39mThis tokenizer cannot be instantiated. Please make sure you have `sentencepiece` installed \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    716\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39min order to use this tokenizer.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    717\u001b[0m             )\n\u001b[1;32m    719\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    720\u001b[0m     \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mUnrecognized configuration class \u001b[39m\u001b[39m{\u001b[39;00mconfig\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m to build an AutoTokenizer.\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m    721\u001b[0m     \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mModel type should be one of \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m, \u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mjoin(c\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m \u001b[39mfor\u001b[39;00m c \u001b[39min\u001b[39;00m TOKENIZER_MAPPING\u001b[39m.\u001b[39mkeys())\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    722\u001b[0m )\n",
+      "\u001b[0;31mValueError\u001b[0m: This tokenizer cannot be instantiated. Please make sure you have `sentencepiece` installed in order to use this tokenizer."
+     ]
+    }
+   ],
+   "source": [
+    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
+    "translator(\"Ce cours est produit par.\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bias and limitations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7aa993970d14ab986e0a7ea1e60087a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67fc2d322c01448b97219733efdaed9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/440M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
+      "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0621be2286144ee296194d349ae8dd5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "674c967d777243819fc3dc85915a1764",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d27abdd37df84243b85e16dea5d6d65e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['carpenter', 'lawyer', 'farmer', 'businessman', 'doctor']\n",
+      "['nurse', 'maid', 'teacher', 'waitress', 'prostitute']\n"
+     ]
+    }
+   ],
+   "source": [
+    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
+    "result = unmasker(\"This man works as a [MASK].\")\n",
+    "print([r[\"token_str\"] for r in result])\n",
+    "\n",
+    "result = unmasker(\"This woman works as a [MASK].\")\n",
+    "print([r[\"token_str\"] for r in result])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "datascience",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}