Instructions to use BanUrsus/distilroberta-base-finetuned-condition-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BanUrsus/distilroberta-base-finetuned-condition-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BanUrsus/distilroberta-base-finetuned-condition-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BanUrsus/distilroberta-base-finetuned-condition-classifier") model = AutoModelForSequenceClassification.from_pretrained("BanUrsus/distilroberta-base-finetuned-condition-classifier") - Notebooks
- Google Colab
- Kaggle
DistilRoBERTa base fintuned condition classifier
Table of Contents
Model Details
Model Description
This model is fine-tuned for a condition classification version of the DistilRoBERTa-base model. This model is case-sensitive: it makes a difference between english and English.
- Fine-tuned by: Ban Ursus
- Model type: Transformer-based language model
- Language(s) (NLP): English
- License: Apache 2.0
- Related Models: DistilRoBERTa-base model
- Resources for more information:
Training Details
This model was fine-tuned 5 epochs using Drug Review Dataset. Therefore, you can improve the accuracy of this model just by training more.
Evaluation
Validation results:
| Accuracy | F1 score |
|---|---|
| 0.63 | 0.58 |
Note: Rounded to 2 decimal places
How to Get Started With the Model
Follow the Section 2 Try it out! of the GitHub Repository.
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