Add st hub-features

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -0,0 +1,74 @@
+---
+pipeline_tag: feature-extraction
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+---
+
+# Sentence Embeddings with `roberta-zwnj-wnli-mean-tokens`
+
+## Usage (Sentence-Transformers)
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+
+sentences = [
+    'اولین حکمران شهر بابل کی بود؟',
+    'در فصل زمستان چه اتفاقی افتاد؟',
+    'میراث کوروش'
+]
+model = SentenceTransformer('m3hrdadfi/roberta-zwnj-wnli-mean-tokens')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+# Max Pooling - Take the max value over time for every dimension. 
+def max_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    token_embeddings[input_mask_expanded == 0] = -1e9  # Set padding tokens to large negative value
+    return torch.mean(token_embeddings, 1)[0]
+
+# Sentences we want sentence embeddings for
+sentences = [
+    'اولین حکمران شهر بابل کی بود؟',
+    'در فصل زمستان چه اتفاقی افتاد؟',
+    'میراث کوروش'
+]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('m3hrdadfi/roberta-zwnj-wnli-mean-tokens')
+model = AutoModel.from_pretrained('m3hrdadfi/roberta-zwnj-wnli-mean-tokens')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+# Perform pooling. In this case, max pooling.
+sentence_embeddings = max_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Questions?
+Post a Github issue from [HERE](https://github.com/m3hrdadfi/sentence-transformers).

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -1,4 +1,4 @@
 {
-  "max_seq_length": null,
+  "max_seq_length": 128,
   "do_lower_case": false
 }