Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use marcolatella/tweet_eval_bench with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcolatella/tweet_eval_bench with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcolatella/tweet_eval_bench")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcolatella/tweet_eval_bench") model = AutoModelForSequenceClassification.from_pretrained("marcolatella/tweet_eval_bench", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from marcolatella/tweet_eval_bench: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/marcolatella/tweet_eval_bench/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://marcolatella/tweet_eval_bench/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/marcolatella/tweet_eval_bench/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 018b871737a4c2f2e06bcd0c7155230b50d446ac1cda45fa979f8f5406d3cf4d
- Size of remote file:
- 268 MB
- SHA256:
- 1579dd8195b1c5ccebcaf25cd7aa525b972cb59abda071a1930caeebb666f29d
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