menadsa / BioS-MiniLM

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Total runs: 460
24-hour runs: -1
7-day runs: -74
30-day runs: 283
Model's Last Updated: March 01 2023
sentence-similarity

Introduction of BioS-MiniLM

Model Details of BioS-MiniLM

{MODEL_NAME}

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark : https://seb.sbert.net

Training

The model was trained with the parameters:

DataLoader :

__main__.PubmedLowMemoryLoader of length 26041 with parameters:

{'batch_size': 128}

Loss :

sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss with parameters:

{'scale': 20.0, 'similarity_fct': 'cos_sim'}

Parameters of the fit()-Method:

{
    "epochs": 1,
    "evaluation_steps": 2000,
    "evaluator": "__main__.PubmedTruePositiveIRetrievalEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 21,
    "weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, '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})
  (2): Normalize()
)
Citing & Authors

Runs of menadsa BioS-MiniLM on huggingface.co

460
Total runs
-1
24-hour runs
-79
3-day runs
-74
7-day runs
283
30-day runs

More Information About BioS-MiniLM huggingface.co Model

BioS-MiniLM huggingface.co

BioS-MiniLM huggingface.co is an AI model on huggingface.co that provides BioS-MiniLM's model effect (), which can be used instantly with this menadsa BioS-MiniLM model. huggingface.co supports a free trial of the BioS-MiniLM model, and also provides paid use of the BioS-MiniLM. Support call BioS-MiniLM model through api, including Node.js, Python, http.

BioS-MiniLM huggingface.co Url

https://huggingface.co/menadsa/BioS-MiniLM

menadsa BioS-MiniLM online free

BioS-MiniLM huggingface.co is an online trial and call api platform, which integrates BioS-MiniLM's modeling effects, including api services, and provides a free online trial of BioS-MiniLM, you can try BioS-MiniLM online for free by clicking the link below.

menadsa BioS-MiniLM online free url in huggingface.co:

https://huggingface.co/menadsa/BioS-MiniLM

BioS-MiniLM install

BioS-MiniLM is an open source model from GitHub that offers a free installation service, and any user can find BioS-MiniLM on GitHub to install. At the same time, huggingface.co provides the effect of BioS-MiniLM install, users can directly use BioS-MiniLM installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

BioS-MiniLM install url in huggingface.co:

https://huggingface.co/menadsa/BioS-MiniLM

Url of BioS-MiniLM

BioS-MiniLM huggingface.co Url

Provider of BioS-MiniLM huggingface.co

menadsa
ORGANIZATIONS

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