up

README.md

@@ -1,16 +1,30 @@
-# Cross-Encoder for Quora Duplicate Questions Detection
+---
+language: en
+pipeline_tag: zero-shot-classification
+tags:
+- distilroberta-base
+datasets:
+- multi_nli
+- snli
+metrics:
+- accuracy
+---
+
+# 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.
 
 ## 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('model_name')
+model = CrossEncoder('cross-encoder/nli-distilroberta-base')
 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
@@ -24,8 +38,8 @@ You can use the model also directly with Transformers library (without SentenceT
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-model = AutoModelForSequenceClassification.from_pretrained('model_name')
+model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-distilroberta-base')
-tokenizer = AutoTokenizer.from_pretrained('model_name')
+tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-distilroberta-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")
 
@@ -36,3 +50,16 @@ with torch.no_grad():
     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-distilroberta-base')
+
+sent = "Apple just announced the newest iPhone X"
+candidate_labels = ["technology", "sports", "politics"]
+res = classifier(sent, candidate_labels)
+print(res)
+```