Instructions to use Ojeda01/bert_base_cased_MultiClass_Sentiment_A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ojeda01/bert_base_cased_MultiClass_Sentiment_A with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ojeda01/bert_base_cased_MultiClass_Sentiment_A")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ojeda01/bert_base_cased_MultiClass_Sentiment_A") model = AutoModelForSequenceClassification.from_pretrained("Ojeda01/bert_base_cased_MultiClass_Sentiment_A", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ojeda01/bert_base_cased_MultiClass_Sentiment_A: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/Ojeda01/bert_base_cased_MultiClass_Sentiment_A/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ojeda01/bert_base_cased_MultiClass_Sentiment_A/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/Ojeda01/bert_base_cased_MultiClass_Sentiment_A/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 11f4e1560f9ba031446e5b3f4b3a154dc22260ce308c2b34cff15a08ab738d9e
- Size of remote file:
- 433 MB
- SHA256:
- d26c470d3da09b689dd73b79db61219b8da0d3228a07ccc568b1d10e1f69c2b6
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