Update README.md

README.md

@@ -11,17 +11,7 @@ metrics:
 license: apache-2.0
 ---
 
-# Cross-Encoder for Natural Language Inference
-This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base)
-
-## Training Data
-The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
-
-## Performance
-- Accuracy on SNLI-test dataset: 92.38
-- Accuracy on  MNLI mismatched set: 90.04
-
-For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
+# Hebrew Cross-Encoder Model
 
 ## Usage
 
@@ -36,25 +26,6 @@ label_mapping = ['contradiction', 'entailment', 'neutral']
 labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
 ```
 
-## Usage with Transformers AutoModel
-You can use the model also directly with Transformers library (without SentenceTransformers library):
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-base')
-tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-base')
-
-features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'],  padding=True, truncation=True, return_tensors="pt")
-
-model.eval()
-with torch.no_grad():
-    scores = model(**features).logits
-    label_mapping = ['contradiction', 'entailment', 'neutral']
-    labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
-    print(labels)
-```
-
 ## Zero-Shot Classification
 This model can also be used for zero-shot-classification:
 ```python
@@ -66,4 +37,6 @@ sent = "Apple just announced the newest iPhone X"
 candidate_labels = ["technology", "sports", "politics"]
 res = classifier(sent, candidate_labels)
 print(res)
-``` 
+```
+
+Sequence