Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use AliChazz/Bert_uncased_fine_tuned_Reward_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AliChazz/Bert_uncased_fine_tuned_Reward_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AliChazz/Bert_uncased_fine_tuned_Reward_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AliChazz/Bert_uncased_fine_tuned_Reward_Model") model = AutoModelForSequenceClassification.from_pretrained("AliChazz/Bert_uncased_fine_tuned_Reward_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:6ccaf656049269601a64dc09c567369b74da1b5705e4a346cd7a68ccf0f48661
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size 437959760
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