Text Classification
Transformers
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use gokulsrinivasagan/bert_uncased_L-4_H-512_A-8_qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokulsrinivasagan/bert_uncased_L-4_H-512_A-8_qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_uncased_L-4_H-512_A-8_qnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_uncased_L-4_H-512_A-8_qnli") model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_uncased_L-4_H-512_A-8_qnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf69acd525ed3ac9fece96f768176a357a61e6d9072ee02ab0860dc762857b9a
- Size of remote file:
- 115 MB
- SHA256:
- 3c85f00496f3f5a5cbdc67fc2e8809e87c1df4588c653957115b81e18a4f0be4
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