Instructions to use Goutham-Vignesh/ContributionSentClassification-scibert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Goutham-Vignesh/ContributionSentClassification-scibert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Goutham-Vignesh/ContributionSentClassification-scibert")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("Goutham-Vignesh/ContributionSentClassification-scibert", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 | |
| # Doc / guide: https://huggingface.co/docs/hub/model-cards | |
| {} | |
| <!-- Performs sentence classification to determine whether a given sentence is a contribution sentence or not from the research paper--> | |
| Performs sentence classification to determine whether a given sentence is a contribution sentence or not from the research paper | |
| ## Model Details | |
| ### Model Description | |
| - **Model type:** text-classification | |
| - **Language(s) (NLP):** EN | |
| - **Finetuned from model:** allenai/scibert_scivocab_uncased | |
| ### How to Get Started with the Model | |
| Use the code below to get started with the model. | |
| ```bash | |
| from transformers import pipeline | |
| from transformers import BertTokenizer, BertForSequenceClassification | |
| model = BertForSequenceClassification.from_pretrained("Goutham-Vignesh/ContributionSentClassification-scibert") | |
| tokenizer=BertTokenizer.from_pretrained('Goutham-Vignesh/ContributionSentClassification-scibert') | |
| text_classification = pipeline('text-classification', model=model, tokenizer=tokenizer) | |
| ``` | |