Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use mp6kv/paper_feedback_intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mp6kv/paper_feedback_intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mp6kv/paper_feedback_intent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mp6kv/paper_feedback_intent") model = AutoModelForSequenceClassification.from_pretrained("mp6kv/paper_feedback_intent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ea0f5525b0c59f53f8fe7637aa9ee7e301b3c78d448ea33900b2f68469ca929
- Size of remote file:
- 499 MB
- SHA256:
- d0873c650742b7e29e6d308b45012996c022a8a450c0beac505b072fee24cff0
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