Instructions to use KalaiselvanD/model_bert_7000_25 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_7000_25 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_7000_25")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_7000_25") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_7000_25", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 253fa7e491d648c3218f144371302cba326669913bf3ae037649d389ed85df4b
- Size of remote file:
- 46.7 MB
- SHA256:
- 34450e865483186fd37bd81b31ac040dbe54196450e8d10a585b8d67d5a5f822
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