Text Classification
Transformers
Safetensors
English
roberta
financial-nlp
sentiment-analysis
distilroberta
active-learning
Eval Results (legacy)
text-embeddings-inference
Instructions to use OMCHOKSI108/FineStream with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OMCHOKSI108/FineStream with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OMCHOKSI108/FineStream")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OMCHOKSI108/FineStream") model = AutoModelForSequenceClassification.from_pretrained("OMCHOKSI108/FineStream", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49e92e4f2eef50086a0a72d86524fa580d8e3077a5572a996eab911521fdd0a0
- Size of remote file:
- 328 MB
- SHA256:
- 31667f355505bb2f76dc4e966015947974bcabf97737445697ec75915e12ce42
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