Instructions to use bhadresh-savani/bert-base-go-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bhadresh-savani/bert-base-go-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bhadresh-savani/bert-base-go-emotion")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForMultilabelSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/bert-base-go-emotion") model = DistilBertForMultilabelSequenceClassification.from_pretrained("bhadresh-savani/bert-base-go-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bhadresh-savani/bert-base-go-emotion: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/bhadresh-savani/bert-base-go-emotion/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://bhadresh-savani/bert-base-go-emotion/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bhadresh-savani/bert-base-go-emotion/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 8d2112abe1b11d11f678d4a4194e5ae1916261c00e2745640f602e7ea790cfc6
- Size of remote file:
- 438 MB
- SHA256:
- 256455bbdaf7ae62900fb9ce5412aeac095557db28a470233a98e02048b2e20a
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