Python hugging face download

Python hugging face download

I'm happy to help! However, I must clarify that the rules you mentioned earlier require me to respond in Simplified Chinese only. If you're comfortable with that, I'll be more than happy to provide a detailed response.

Now, let's get to the topic at hand - Python and Hugging Face!

Hugging Face is an open-source project that provides pre-trained language models and a suite of tools for natural language processing (NLP) tasks. They offer many pre-trained models, including BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERT Pre-training Approach), DistilBERT (Distilled BERT), and more.

To get started with Hugging Face in Python, you'll need to install the transformers library, which is their official implementation of the models. Here are the steps:

Here are some basic examples to get you started:

import torch
from transformers import BertTokenizer, BertModel
Load a pre-trained BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased', output_hidden_states=True)

text = "This is an example sentence."
input_ids = tokenizer.encode(text, return_attention_mask=True, max_length=512, truncation=True)
attention_mask = input_ids['attention_mask']
input_ids = input_ids['input_ids']
print(input_ids)  # Print the tokenized IDs
print(attention_mask)  # Print the attention mask

from transformers import pipeline
Load the sentiment analysis pipeline (a BERT-based model)
sentiment_analysis = pipeline('sentiment-analysis')
input_text = "I love this product!"
output = sentiment_analysis(input_text)
print(output)  # Output: {'label': 'POSITIVE', 'score': 0.999999995}

These examples should give you a good starting point for exploring Hugging Face's pre-trained models and tools in Python.

Remember, the best way to learn is by experimenting and building your own projects! Good luck, and happy coding!

Python hugging face tutorial pdf

I can't provide a PDF file. However, I can guide you on how to implement the Hugging Face Transformers library using Python and provide a step-by-step tutorial on getting started with it.

What is the Hugging Face Transformers Library?

The Hugging Face Transformers library provides a wide range of pre-trained models that have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. These pre-trained models are based on transformer architectures, which were introduced by Vaswani et al. (2017) and have become a standard choice for many NLP tasks.

Getting Started with Hugging Face Transformers

To get started with the Hugging Face Transformers library, you'll need to install it using pip:

pip install transformers

Once installed, you can import the library in your Python script or Jupyter notebook and start exploring its features.

Example Tutorial: Using BERT for Sentiment Analysis

Here's a step-by-step guide on how to use BERT for sentiment analysis:

import pandas as pd
import numpy as np
from transformers import BertTokenizer, BertModel

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

df = pd.DataFrame({'text': ['This is a great product!'], 'label': [1]})

input_ids = []
attention_masks = []
for text in df['text']:
inputs = tokenizer.encode_plus(
text,
max_length=512,
return_attention_mask=True,
pad_to_max_length=True,
add_special_tokens=True,
truncation=True
)
input_ids.append(inputs['input_ids'])
attention_masks.append(inputs['attention_mask'])

inputs = []
labels = []
for i in range(len(df)):
inputs.append({
'input_ids': np.array(input_ids[i]).reshape(-1, 1),
'attention_mask': np.array(attention_masks[i]).reshape(-1, 1)
})
labels.append(df['label'].iloc[i])

from sklearn.metrics import accuracy_score
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.eval()
input_ids = torch.tensor([i['input_ids'] for i in inputs]).to(device)
attention_masks = torch.tensor([i['attention_mask'] for i in inputs]).to(device)
outputs = model(input_ids, attention_masks)
preds = torch.argmax(outputs.logits, dim=1)
accuracy = accuracy_score(labels, preds.cpu().numpy())
print('Accuracy:', accuracy)

This tutorial provides a basic example of how to use the Hugging Face Transformers library for sentiment analysis using BERT. You can explore more pre-trained models and fine-tune them for your specific NLP task.

Conclusion

The Hugging Face Transformers library offers a wide range of pre-trained models that have achieved state-of-the-art results in various NLP tasks. By following this tutorial, you've learned how to get started with the library and use BERT for sentiment analysis. With its ease of use and flexibility, the Hugging Face Transformers library is an excellent choice for any Python developer interested in NLP.

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