README.md

---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers

---

# tomaarsen/distilroberta-base-nli-gist

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Some post-training logs:
```
2024-04-09 15:33:55 - EmbeddingSimilarityEvaluator: Evaluating the model on sts-dev dataset after epoch 0:                                                           
2024-04-09 15:33:56 - Cosine-Similarity :       Pearson: 0.8567 Spearman: 0.8662                                                                                     
2024-04-09 15:33:56 - Manhattan-Distance:       Pearson: 0.8540 Spearman: 0.8534
2024-04-09 15:33:56 - Euclidean-Distance:       Pearson: 0.8572 Spearman: 0.8571
2024-04-09 15:33:56 - Dot-Product-Similarity:   Pearson: 0.6507 Spearman: 0.6496
```
```
2024-04-09 15:33:56 - EmbeddingSimilarityEvaluator: Evaluating the model on sts-test dataset:
2024-04-09 15:33:56 - Cosine-Similarity :       Pearson: 0.8294 Spearman: 0.8426
2024-04-09 15:33:56 - Manhattan-Distance:       Pearson: 0.8274 Spearman: 0.8225
2024-04-09 15:33:56 - Euclidean-Distance:       Pearson: 0.8303 Spearman: 0.8255
2024-04-09 15:33:56 - Dot-Product-Similarity:   Pearson: 0.5832 Spearman: 0.5722
```
The `Cosine-Similarity`, `Spearman` scores are most important.

<!--- Describe your model here -->

## 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('tomaarsen/distilroberta-base-nli-gist')
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('tomaarsen/distilroberta-base-nli-gist')
model = AutoModel.from_pretrained('tomaarsen/distilroberta-base-nli-gist')

# 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=tomaarsen/distilroberta-base-nli-gist)


## Training
The model was trained with the parameters:

**DataLoader**:

`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 4403 with parameters:
```
{'batch_size': 128}
```

**Loss**:

`sentence_transformers.losses.GISTEmbedLoss.GISTEmbedLoss` with parameters:
  ```
  {'guide': SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, '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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
), 'temperature': 0.01}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 1,
    "evaluation_steps": 440,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 441,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->