Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use masapasa/sagemaker-distilbert-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use masapasa/sagemaker-distilbert-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="masapasa/sagemaker-distilbert-emotion")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("masapasa/sagemaker-distilbert-emotion") model = AutoModelForSequenceClassification.from_pretrained("masapasa/sagemaker-distilbert-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from masapasa/sagemaker-distilbert-emotion: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/masapasa/sagemaker-distilbert-emotion/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://masapasa/sagemaker-distilbert-emotion/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/masapasa/sagemaker-distilbert-emotion/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 0c134504706c651ed2570bc95893e244030a498eaefb3b090587acd01971cca4
- Size of remote file:
- 268 MB
- SHA256:
- 4ff71ab8fe836284b81a0044aa167c6fb2a786351cc7e1cc592d8c47f8b56367
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