---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: sgpt-bloom-1b7-nli
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (fr)
      config: fr
      split: test
      revision: c379a6705fec24a2493fa68e011692605f44e119
    metrics:
    - type: accuracy
      value: 39.286
    - type: f1
      value: 38.87078070073539
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (zh)
      config: zh
      split: test
      revision: c379a6705fec24a2493fa68e011692605f44e119
    metrics:
    - type: accuracy
      value: 37.634
    - type: f1
      value: 36.86046604093418
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (fr)
      config: fr
      split: test
      revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3
    metrics:
    - type: accuracy
      value: 83.79893517068588
    - type: f1
      value: 83.72326662566203
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (fr)
      config: fr
      split: test
      revision: 6299947a7777084cc2d4b64235bf7190381ce755
    metrics:
    - type: accuracy
      value: 63.36047604134043
    - type: f1
      value: 44.261707019308126
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (fr)
      config: fr
      split: test
      revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea
    metrics:
    - type: accuracy
      value: 64.57632817753867
    - type: f1
      value: 62.60453982786661
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (fr)
      config: fr
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 69.59986550100874
    - type: f1
      value: 69.71803697939914
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (zh)
      config: zh
      split: test
      revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
    metrics:
    - type: cos_sim_pearson
      value: 59.71781185663265
    - type: cos_sim_spearman
      value: 58.538648447630514
    - type: euclidean_pearson
      value: 53.53848180206165
    - type: euclidean_spearman
      value: 56.33730262964236
    - type: manhattan_pearson
      value: 54.62109820575505
    - type: manhattan_spearman
      value: 57.223846291318914
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (fr)
      config: fr
      split: test
      revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906
    metrics:
    - type: cos_sim_pearson
      value: 73.44021434651606
    - type: cos_sim_spearman
      value: 73.13412769502769
    - type: euclidean_pearson
      value: 68.16368597409867
    - type: euclidean_spearman
      value: 72.44964781564485
    - type: manhattan_pearson
      value: 69.42307032478939
    - type: manhattan_spearman
      value: 73.3523195012387
---

# sgpt-bloom-1b7-nli

## Usage

For usage instructions, refer to: https://github.com/Muennighoff/sgpt#symmetric-semantic-search

The model was trained with the command
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 accelerate launch examples/training/nli/training_nli_v2.py --model_name bigscience/bloom-1b3 --freezenonbias --train_batch_size 128 --lr 32e-5 --pooling weightedmean --wandb --wandbwatchlog gradients --gradcache --chunksize 4
```

## Evaluation Results

`{'askubuntu': 57.44, 'cqadupstack': 14.18, 'twitterpara': 73.99, 'scidocs': 74.74, 'avg': 55.087500000000006}`

## Training
The model was trained with the parameters:

**DataLoader**:

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

The model uses BitFit, weighted-mean pooling & GradCache, for details see: https://arxiv.org/abs/2202.08904

**Loss**:

`sentence_transformers.losses.MultipleNegativesRankingLoss.MNRLGradCache` 

Parameters of the fit()-Method:
```
{
    "epochs": 1,
    "evaluation_steps": 440,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 0.00032
    },
    "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: BloomModel 
  (1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```

## Citing & Authors

```bibtex
@article{muennighoff2022sgpt,
  title={SGPT: GPT Sentence Embeddings for Semantic Search},
  author={Muennighoff, Niklas},
  journal={arXiv preprint arXiv:2202.08904},
  year={2022}
}
```