README.md

@@ -1,31 +1,16 @@
----
-language: en
-pipeline_tag: zero-shot-classification
-tags:
-- roberta-base
-datasets:
-- multi_nli
-- snli
-metrics:
-- accuracy
-license: apache-2.0
----
-
-# Cross-Encoder for Natural Language Inference
+# Cross-Encoder for Quora Duplicate Questions Detection
 This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
 
 ## 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
-For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
 
 ## Usage
 
 Pre-trained models can be used like this:
 ```python
 from sentence_transformers import CrossEncoder
-model = CrossEncoder('cross-encoder/nli-roberta-base')
+model = CrossEncoder('model_name')
 scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
 
 #Convert scores to labels
@@ -39,8 +24,8 @@ You can use the model also directly with Transformers library (without SentenceT
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-roberta-base')
-tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-roberta-base')
+model = AutoModelForSequenceClassification.from_pretrained('model_name')
+tokenizer = AutoTokenizer.from_pretrained('model_name')
 
 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")
 
@@ -50,17 +35,4 @@ with torch.no_grad():
     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
-from transformers import pipeline
-
-classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-roberta-base')
-
-sent = "Apple just announced the newest iPhone X"
-candidate_labels = ["technology", "sports", "politics"]
-res = classifier(sent, candidate_labels)
-print(res)
-``` 
+```