Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use marcolatella/emotion_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcolatella/emotion_trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcolatella/emotion_trained")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcolatella/emotion_trained") model = AutoModelForSequenceClassification.from_pretrained("marcolatella/emotion_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from marcolatella/emotion_trained: direct link, hf CLI and curl.
- Browser
- Download file 2.93 kB
-
https://huggingface.co/marcolatella/emotion_trained/resolve/main/training_args.bin
- Command line
-
hf download hf://marcolatella/emotion_trained/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/marcolatella/emotion_trained/resolve/main/training_args.bin
2.93 kB
- Xet hash:
- 8be0d773e7d1522c2f1f8a9d0ecc535e8ba57d67b0b230377f928daadbe72e5b
- Size of remote file:
- 2.93 kB
- SHA256:
- 801a10ef17581083c33a561a47ab2cd77281a0757bb9ee3c5f3b203d89700d14
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.