Text Classification
Transformers
Safetensors
English
bert
mnli
multinli
natural-language-inference
sequence-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use Lidor-Mashiach/bert-base-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lidor-Mashiach/bert-base-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Lidor-Mashiach/bert-base-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Lidor-Mashiach/bert-base-mnli") model = AutoModelForSequenceClassification.from_pretrained("Lidor-Mashiach/bert-base-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 271 Bytes
93fd8a3 | 1 2 3 4 5 6 7 8 9 10 | {
"model": "BERT-base",
"dataset": "MNLI",
"validation_accuracy": 0.839429444727458,
"validation_examples": 9815,
"validation_split": "validation_matched",
"test_accuracy": 0.8444873881204231,
"test_examples": 9832,
"test_split": "validation_mismatched"
} |