Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use AndrewDOrlov/bert-eval-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndrewDOrlov/bert-eval-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AndrewDOrlov/bert-eval-256")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AndrewDOrlov/bert-eval-256") model = AutoModelForSequenceClassification.from_pretrained("AndrewDOrlov/bert-eval-256", 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:c31d3076667c3f7723e2ecd37fa7ab766ae7b34a1d608e9872d0ec03e4e5ffa2
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size 711478408
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