Instructions to use KalaiselvanD/model_bert_check_new_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_check_new_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_check_new_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_check_new_2") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_check_new_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0cfcc8999e02245045d51c37acac4d5ea8840bcd53b35bca94e98eda1e178de
- Size of remote file:
- 46.7 MB
- SHA256:
- 624b08babae3df1d41ec58e805c70be8bc95837fb670f1e22bf3a63453e68e86
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.