Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use DarshanDeshpande/distilbert_eli5_reward_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DarshanDeshpande/distilbert_eli5_reward_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DarshanDeshpande/distilbert_eli5_reward_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DarshanDeshpande/distilbert_eli5_reward_model") model = AutoModelForSequenceClassification.from_pretrained("DarshanDeshpande/distilbert_eli5_reward_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from DarshanDeshpande/distilbert_eli5_reward_model: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/DarshanDeshpande/distilbert_eli5_reward_model/resolve/main/model.safetensors
- Command line
-
hf download hf://DarshanDeshpande/distilbert_eli5_reward_model/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/DarshanDeshpande/distilbert_eli5_reward_model/resolve/main/model.safetensors
268 MB
- Xet hash:
- 3675dfb6e133b4d7cbf11b884f96159deac2f2a783b45e86197588a09319ec83
- Size of remote file:
- 268 MB
- SHA256:
- 2980318ac204b1e20096bba22e45546ead595defa652a91cd2d9b97bf92b5c61
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