Delete README.me

README.me

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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