Instructions to use Harsh-7300/bert_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harsh-7300/bert_trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Harsh-7300/bert_trained")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Harsh-7300/bert_trained") model = AutoModelForSequenceClassification.from_pretrained("Harsh-7300/bert_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 128 Bytes
a6b69c6 | 1 2 3 4 5 6 7 8 | {
"model_type": "bert",
"do_lower_case": true,
"strip_accents": false,
"pad_token_id": 0,
"wordpieces_prefix": "##"
}
|