Quelle classifier.ipynb Sprache: unbekannt
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Spracherkennung für: .ipynb vermutete Sprache: SPS {SPS[36] Python[27] R[18]} [Methode: maximale Elemente, drei Dimensionen]
{
"cells": [
{
"cell_type": "markdown",
"id": "0f47fc4b-7cdc-48ce-b42c-e0b73d902ece",
"metadata": {},
"source": [
"# Train a classifier with Mistral 7B"
]
},
{
"cell_type": "markdown",
"id": "bfd6bdfb-c8cc-4fb6-8969-153a97dfb576",
"metadata": {},
"source": [
"In this tutorial, we will see how we can leverage Mistral 7B to train a classifier. We need:\n",
"- The mistral codebase: `https://github.com/mistralai/mistral-inference`\n",
"- The pretrained model and its tokenizer: `https://docs.mistral.ai/llm/mistral-v0.1`\n",
"- A dataset to train our classifier\n",
"\n",
"We will train and evaluate the classifier on `Symptom2Disease`, a public classification dataset from Kaggle provided in a CSV format: https://www.kaggle.com/datasets/niyarrbarman/symptom2disease/"
]
},
{
"cell_type": "markdown",
"id": "225f88da-96ab-4262-88f8-8f1d6d5224bd",
"metadata": {},
"source": [
"## Set paths"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ebe4bc9b-d0f7-42a6-9e41-bb3d042e0e07",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"code_path = \"/codebase_path/mistral-inference\ " # codebase\n",
"data_path = Path(\"/dataset_path/Symptom2Disease.csv\ ") # dataset downloaded from Kaggle\n",
"model_path = Path(\"/model_path/\ ") # model and tokenizer location"
]
},
{
"cell_type": "markdown",
"id": "3fcc420b-2ac7-42e1-a3b1-8a80f87ccc10",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5e8227fe-d2fb-4a2f-a136-96ee2d32287f",
"metadata": {},
"outputs": [],
"source": [
"import csv\n",
"import tqdm\n",
"import torch\n",
"import numpy as np\n",
"\n",
"import sys\n",
"sys.path.append(code_path) # append the path where mistral-inference was cloned\n",
"\n",
"from mistral.model import Transformer\n",
"from mistral_common.tokens.tokenizers.mistral import MistralTokenizer"
]
},
{
"cell_type": "markdown",
"id": "3b9c8408-1bc1-4e79-8b6d-391e07d78eb4",
"metadata": {},
"source": [
"## Load the model and tokenizer"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "806ff308-5909-46f7-b10e-8b1f1c7b7edc",
"metadata": {},
"outputs": [],
"source": [
"model = Transformer.from_folder(model_path, dtype=torch.bfloat16)\n",
"mistral_tokenizer = MistralTokenizer.from_file(str(model_path / \"tokenizer.model\ "))\n",
"tokenizer = mistral_tokenizer.instruct_tokenizer.tokenizer"
]
},
{
"cell_type": "markdown",
"id": "4a9aa34b-cbe3-49a6-948b-17f6c84d93c6",
"metadata": {},
"source": [
"The tokenizer is a critical component of the model that segments input text into word-pieces.\n",
"\n",
"For instance, the sentence `\"Roasted barramundi fish\ "` is encoded as `['▁Ro', 'asted', '▁barr', 'am', 'und', 'i', '▁fish']`.\n",
"\n",
"The number of subwords in the model is set to `32000`, and each word is decomposed into known word pieces to avoid unknown words."
]
},
{
"cell_type": "markdown",
"id": "ce0e03ef-439f-4078-a848-d69bf4e339ec",
"metadata": {},
"source": [
"## Load the dataset"
]
},
{
"cell_type": "markdown",
"id": "65d1f696-138d-467d-bb53-032e08d47bf1",
"metadata": {},
"source": [
"We reload the Kaggle dataset from the disk. Each sample is composed of a sentence and a label. The dataset contains 24 different labels."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cc2bf18b-d99c-4c31-9988-1675d019e04b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reloaded 1200 samples with 24 labels.\n"
]
}
],
"source": [
"data = [] # list of (text, label)\n",
"with open(data_path, newline='') as csvfile:\n",
" reader = csv.reader(csvfile)\n",
" for i, row in enumerate(reader):\n",
" if i == 0: # skip csv header\n",
" continue\n",
" data.append((row[2], row[1]))\n",
"\n",
"# label text to label ID\n",
"labels = sorted({x[1] for x in data})\n",
"txt_to_label = {x: i for i, x in enumerate(labels)}\n",
"print(f\"Reloaded { len(data)} samples with { len(labels)} labels.\ ")\n",
"\n",
"# integer class for each datapoint\n",
"data_class = [txt_to_label[x[1]] for x in data]"
]
},
{
"cell_type": "markdown",
"id": "c5434265-fad8-4daa-b54b-438d420bcb4d",
"metadata": {},
"source": [
"The task is to classify a symptom, for instance `\"I have a dry cough that never stops.\ "` to one of the 24 disease labels (`Acne, Arthritis, Bronchial Asthma, Cervical spondylosis, Chicken pox, Common Cold, etc.`)."
]
},
{
"cell_type": "markdown",
"id": "1540b537-c3cc-4851-80e3-91a9b9e8cc7c",
"metadata": {},
"source": [
"## Embed data points in the dataset"
]
},
{
"cell_type": "markdown",
"id": "c73d5d75-55eb-4210-8ffe-d0db3e9f8735",
"metadata": {},
"source": [
"We will now learn a linear classifier on frozen features provided by Mistral 7B.\n",
"In particular, each sentence in the dataset will be tokenized, and provided to the model.\n",
"\n",
"If the input sentence is composed of `N` tokens, the model output will be a list of `N` vectors of dimension `d=4096`, where `d` is the dimensionality of the model.\n",
"These vectors are then averaged along the dimension 0 to get a vector of size `d`.\n",
"\n",
"Finally, the vectors of all sentences in the dataset are concatenated into a matrix of shape `(D, d)` where `D` is the number of samples in the dataset (in particular, D=1200)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "78019021-01d7-499f-94ea-a3c88bbcb42c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"1200it [00:25, 46.65it/s]\n"
]
}
],
"source": [
"with torch.no_grad():\n",
" featurized_x = []\n",
" # compute an embedding for each sentence\n",
" for i, (x, y) in tqdm.tqdm(enumerate(data)):\n",
" tokens = tokenizer.encode(x, bos=True)\n",
" tensor = torch.tensor(tokens).to(model.device)\n",
" features = model.forward_partial(tensor, [len(tokens)]) # (n_tokens, model_dim)\n",
" featurized_x.append(features.float().mean(0).cpu().detach().numpy())\n",
"\n",
"# concatenate sentence embeddings\n",
"X = np.concatenate([x[None] for x in featurized_x], axis=0) # (n_points, model_dim)"
]
},
{
"cell_type": "markdown",
"id": "7a997101-7844-4928-bdf4-d7a3051eca75",
"metadata": {},
"source": [
"## Plot t-SNE embeddings"
]
},
{
"cell_type": "markdown",
"id": "61bbc6b4-dfc5-42e0-b99b-1ed281d929cb",
"metadata": {},
"source": [
"Now that we have one vector/embedding for each sample in our dataset, we can visualize them using t-SNE.\n",
"t-SNE is a powerful tool for reducing the dimensionality of high-dimensional data, while preserving the underlying structure and relationships between the data points, which can help uncover patterns and insights that would be difficult to discern in the high-dimensional space.\n",
"\n",
"In the graph below, we assign a different color to each class in the dataset. It is apparent that several distinct clusters are visible in the data."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8e3cd894-ac30-4f34-a4c8-ef34293b587d",
"metadata": {},
"outputs": [
{
"data": {
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