Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use diegofiggie/empathy_task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use diegofiggie/empathy_task with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("diegofiggie/empathy_task") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use diegofiggie/empathy_task with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("diegofiggie/empathy_task") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download model_head.pkl from diegofiggie/empathy_task: direct link, hf CLI and curl.
- Browser
- Download file 3.81 kB
-
https://huggingface.co/diegofiggie/empathy_task/resolve/main/model_head.pkl
- Command line
-
hf download hf://diegofiggie/empathy_task/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/diegofiggie/empathy_task/resolve/main/model_head.pkl
3.81 kB
- Xet hash:
- 158a47ed37f2c2835d782d5481f73818c8eeddbba0f7259bac271f0d517d683a
- Size of remote file:
- 3.81 kB
- SHA256:
- 3179b7cf4f42b0be7cd3fab59bbc7a5ea292ee4ba5aaf986f4036344c3b1ab55
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