bobox / E5-base-unsupervised-TSDAE

SentenceTransformer based on intfloat/e5-base-unsupervised

This is a sentence-transformers model finetuned from intfloat/e5-base-unsupervised . It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: intfloat/e5-base-unsupervised
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("bobox/E5-base-unsupervised-TSDAE")
# Run inference
sentences = [
    "should eat diarrhea should solid as soon able you're bottle your have, try to them as . at home until 48 last spreading others.",
    "how long should you wait to eat after having diarrhea? You should eat solid food as soon as you feel able to. If you're breastfeeding or bottle feeding your baby and they have diarrhoea, you should try to feed them as normal. Stay at home until at least 48 hours after the last episode of diarrhoea to prevent spreading any infection to others.",
    'how to copy multiple cells in excel and paste?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

• Dataset: sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 300,000 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 3 tokens mean: 20.46 tokens max: 69 tokens	min: 8 tokens mean: 47.85 tokens max: 132 tokens

• Samples:

  sentence_0	sentence_1
  matter An unit of retains all subatomic neutrons Hydrogen (one one neutrons	are particles of matter atoms? An atom is the smallest unit of matter that retains all of the chemical properties of an element. ... Most atoms contain all three of these types of subatomic particles—protons, electrons, and neutrons. Hydrogen (H) is an exception because it typically has one proton and one electron, but no neutrons.
  equals how	5 ml equals how many ounces?
  "A Country Boy School is poor is forced to its boy to school following official, ignoring mean a jail	"A Country Boy Quits School" by Lao Hsiang is an endearing social satire. It is about a poor Chinese family which is forced to send its boy to school following an official proclamation, ignoring which would mean a jail term.

• Loss: DenoisingAutoEncoderLoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy : steps
• per_device_train_batch_size : 14
• per_device_eval_batch_size : 14
• num_train_epochs : 1
• multi_dataset_batch_sampler : round_robin

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.10.13
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.1.2
• Accelerate: 0.31.0
• Datasets: 2.19.2
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

DenoisingAutoEncoderLoss

@inproceedings{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", 
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    pages = "671--688",
    url = "https://arxiv.org/abs/2104.06979",
}