Fix: Add missing sentence-transformers installation in example code

README.md

@@ -1,72 +1,73 @@
----
-language: en
-pipeline_tag: zero-shot-classification
-tags:
-- transformers
-datasets:
-- nyu-mll/multi_nli
-- stanfordnlp/snli
-metrics:
-- accuracy
-license: apache-2.0
-base_model:
-- microsoft/deberta-v3-large
-library_name: sentence-transformers
----
-
-# 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-large](https://huggingface.co/microsoft/deberta-v3-large)
-
-## 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: 92.20
-- Accuracy on  MNLI mismatched set: 90.49
-
-For futher 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-deberta-v3-large')
-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
-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-large')
-tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-large')
-
-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
-from transformers import pipeline
-
-classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-large')
-
-sent = "Apple just announced the newest iPhone X"
-candidate_labels = ["technology", "sports", "politics"]
-res = classifier(sent, candidate_labels)
-print(res)
+---
+language: en
+pipeline_tag: zero-shot-classification
+tags:
+- transformers
+datasets:
+- nyu-mll/multi_nli
+- stanfordnlp/snli
+metrics:
+- accuracy
+license: apache-2.0
+base_model:
+- microsoft/deberta-v3-large
+library_name: sentence-transformers
+---
+
+# 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-large](https://huggingface.co/microsoft/deberta-v3-large)
+
+## 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: 92.20
+- Accuracy on  MNLI mismatched set: 90.49
+
+For futher 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
+# Requires: sentence-transformers
+from sentence_transformers import CrossEncoder
+model = CrossEncoder('cross-encoder/nli-deberta-v3-large')
+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
+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-large')
+tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-large')
+
+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
+from transformers import pipeline
+
+classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-large')
+
+sent = "Apple just announced the newest iPhone X"
+candidate_labels = ["technology", "sports", "politics"]
+res = classifier(sent, candidate_labels)
+print(res)
 ```