Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use AndrewDOrlov/bert-eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndrewDOrlov/bert-eval with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AndrewDOrlov/bert-eval")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AndrewDOrlov/bert-eval") model = AutoModelForSequenceClassification.from_pretrained("AndrewDOrlov/bert-eval", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d8b78e0f3033a813c50eeecf7719f0a09a57253b6a8b22125a0124ad08da23b
- Size of remote file:
- 3.9 kB
- SHA256:
- 994abc6f1e628aa6c0ec7883b4f7b1e592919f97c069b72bf523c7feadc1d5b3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.