Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use GhylB/Sentiment_Analysis_BERT_Based_MODEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GhylB/Sentiment_Analysis_BERT_Based_MODEL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GhylB/Sentiment_Analysis_BERT_Based_MODEL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GhylB/Sentiment_Analysis_BERT_Based_MODEL") model = AutoModelForSequenceClassification.from_pretrained("GhylB/Sentiment_Analysis_BERT_Based_MODEL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from GhylB/Sentiment_Analysis_BERT_Based_MODEL: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/GhylB/Sentiment_Analysis_BERT_Based_MODEL/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://GhylB/Sentiment_Analysis_BERT_Based_MODEL/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/GhylB/Sentiment_Analysis_BERT_Based_MODEL/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- efd2b4642f999ffd225bc6c97990f667a621c016cfb8e0a5670aa5938eaf7dff
- Size of remote file:
- 433 MB
- SHA256:
- 859b9dec359a67285f07ea59da8ea2af976b1382d17a73fb5efaf7aeb2cda5a9
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