Update README.md

README.md

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 ---
 license: apache-2.0
+datasets: 
+- multi_nli
+- pietrolesci/nli_fever
+pipeline_tag: text-classification
+tags:
+- feature-extraction
+- sentence-similarity
+- transformers
+language:
+- en
+- nl
+- de
+- fr
+- it
+- es
 ---
+
+# Cross-Encoder for Sentence Similarity
+This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
+
+## Training Data
+This model was trained on 6 different nli datasets. The model will predict a score between 0 (not similar) and 1 (very similar) for the semantic similarity of two sentences. 
+
+
+## Usage (CrossEncoder)
+Comparing each sentence of sentences1 array to the corrosponding sentence of sentences2 array like comparing the first sentnece of each array, then comparing the second sentence of each array,...
+```python
+from sentence_transformers import CrossEncoder
+
+
+model = CrossEncoder('abbasgolestani/ag-nli-DeTS-sentence-similarity-v3-light')
+
+# Two lists of sentences
+sentences1 = ['I am honored to be given the opportunity to help make our company better',
+             'I love my job and what I do here',
+             'I am excited about our company’s vision']
+
+sentences2 = ['I am hopeful about the future of our company',
+              'My work is aligning with my passion',
+              'Definitely our company vision will be the next breakthrough to change the world and I’m so happy and proud to work here']
+
+pairs = zip(sentences1,sentences2)
+list_pairs=list(pairs)
+
+scores1 = model.predict(list_pairs, show_progress_bar=False)
+print(scores1)
+
+for i in range(len(sentences1)):
+    print("{} \t\t {} \t\t Score: {:.4f}".format(sentences1[i], sentences2[i], scores1[i]))
+
+```
+
+
+
+
+
+## Usage #2
+
+Pre-trained models can be used like this:
+```python
+from sentence_transformers import CrossEncoder
+model = CrossEncoder('abbasgolestani/ag-nli-DeTS-sentence-similarity-v3-light')
+scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
+```
+
+The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
+
+You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class