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# {nemesis-gte-tiny}

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. 

It is fine-tuned from [`TaylorAI/gte-tiny`](https://huggingface.co/TaylorAI/gte-tiny) on public documents processed through a [Nemesis](https://github.com/SpecterOps/Nemesis) pipeline. The ~2500 documents were chunked into 512 tokens and submitted to Gemini for Question/Answer generation. Each query generated 2 questions, and the entire process was executed twice, resulting in ~10k questions generated for context chunks.

The positive chunks were linked to each qusetion and 5 random text chunks from documents _other_ than the source were used as the negative training samples. We followed the guide from [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/README.md) for fine tuning. The fine tuned model was merged back with the `TaylorAI/gte-tiny` base using [LM_Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) as the guide described.


## 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 = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('Harmj0y/nemesis-gte-tiny')
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


#Mean Pooling - Take attention mask into account for correct averaging
def mean_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()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Harmj0y/nemesis-gte-tiny')
model = AutoModel.from_pretrained('Harmj0y/nemesis-gte-tiny')

# 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, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```



## Evaluation Results

<!--- Describe how your model was evaluated -->

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})



## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

Fine tuned from [TaylorAI/gte-tiny](https://huggingface.co/TaylorAI/gte-tiny/) using [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/)'s [embedding fine-tuning guide](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/README.md).