Text Classification
Transformers
Safetensors
mistral
trl
reward-trainer
Generated from Trainer
text-embeddings-inference
Instructions to use bidit/mistral-reward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bidit/mistral-reward with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bidit/mistral-reward")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bidit/mistral-reward") model = AutoModelForSequenceClassification.from_pretrained("bidit/mistral-reward", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6cbab5a615aacf07ace90c9616a0aa9a00cbdd7e5ed966d518d2fc2135a649d0
- Size of remote file:
- 5.3 kB
- SHA256:
- 5cbd07f61dcd67fa9f4bd70ce6530c8dded620a3d7bace11cecbe5995705a058
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