Instructions to use mayapapaya/Keyword-Extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mayapapaya/Keyword-Extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mayapapaya/Keyword-Extractor")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mayapapaya/Keyword-Extractor") model = AutoModelForSequenceClassification.from_pretrained("mayapapaya/Keyword-Extractor") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
This model is meant to extract keywords from text.
- Model type: text-classification
- Language(s) (NLP): English
- License: cc
- Finetuned from model [optional]: [More Information Needed]
Training Details
This model is a fine-tuned version of the distilbert-base-uncased-finetuned-sst-2-english model.
Training Data
Trained on 51la5/keyword-extraction from HuggingFace Hub.
How to Get Started with the Model
Note: model inputs were tokenized using distilbert-base-uncased tokenizer
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("mayapapaya/Keyword-Extractor")