Text Classification
Transformers
Safetensors
English
bert
snli
natural-language-inference
sequence-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use Lidor-Mashiach/bert-base-snli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lidor-Mashiach/bert-base-snli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Lidor-Mashiach/bert-base-snli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Lidor-Mashiach/bert-base-snli") model = AutoModelForSequenceClassification.from_pretrained("Lidor-Mashiach/bert-base-snli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 247 Bytes
d96cae8 | 1 2 3 4 5 6 7 8 9 10 | {
"model": "BERT-base",
"dataset": "SNLI",
"validation_accuracy": 0.9104856736435684,
"validation_examples": 9842,
"validation_split": "validation",
"test_accuracy": 0.9087947882736156,
"test_examples": 9824,
"test_split": "test"
} |