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README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- causal-lm
+license:
+- cc-by-sa-4.0
+---
+
+# TODO: Name of Model
+
+TODO: Description
+
+## Model Description
+TODO: Add relevant content
+
+(0) Base Transformer Type: RobertaModel
+
+(1) Pooling mean
+
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence"]
+
+model = SentenceTransformer(TODO)
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+## Usage (HuggingFace Transformers)
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# The next step is optional if you want your own pooling function.
+# 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
+    max_over_time = torch.max(token_embeddings, 1)[0]
+    return max_over_time
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained(TODO)
+model = AutoModel.from_pretrained(TODO)
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, 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)
+```
+
+
+
+## TODO: Training Procedure
+
+## TODO: Evaluation Results
+
+## TODO: Citing & Authors