Text Classification
Transformers
PyTorch
TensorBoard
English
roberta
financial-sentiment-analysis
sentiment-analysis
sentence_50agree
Generated from Trainer
sentiment
finance
Eval Results (legacy)
text-embeddings-inference
Instructions to use nickmuchi/distilroberta-finetuned-financial-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickmuchi/distilroberta-finetuned-financial-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nickmuchi/distilroberta-finetuned-financial-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nickmuchi/distilroberta-finetuned-financial-text-classification") model = AutoModelForSequenceClassification.from_pretrained("nickmuchi/distilroberta-finetuned-financial-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a80aea391c3997ea34b59e959230f7471e35ab10229a97cc179ea70de48c37ff
- Size of remote file:
- 2.99 kB
- SHA256:
- a175420304e2551c17e7b215377aea73147f6e948eae0c9c31e403430038bcc1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.