Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use aXhyra/sentiment_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aXhyra/sentiment_trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aXhyra/sentiment_trained")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aXhyra/sentiment_trained") model = AutoModelForSequenceClassification.from_pretrained("aXhyra/sentiment_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aXhyra/sentiment_trained: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/aXhyra/sentiment_trained/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aXhyra/sentiment_trained/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aXhyra/sentiment_trained/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- bc7af9e6eb3185524139c5cbbdc6701eb36cb0279fdc13269178b827dbc4a089
- Size of remote file:
- 268 MB
- SHA256:
- 125b9972e23df4fc753cffef41bf97fa3a441850f7761294758e8b78de5bdea7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.