AISE-TUDelft/python-parameters-classifier

1---2pipeline_tag: sentence-similarity3tags:4- sentence-transformers5- feature-extraction6- sentence-similarity7 8---9 10# {MODEL_NAME}11 12This 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.13 14[Link to arXiv paper](https://arxiv.org/abs/2302.13149)15 16<!--- Describe your model here -->17 18## Usage (Sentence-Transformers)19 20Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:21 22```23pip install -U sentence-transformers24```25 26Then you can use the model like this:27 28```python29from sentence_transformers import SentenceTransformer30sentences = ["This is an example sentence", "Each sentence is converted"]31 32model = SentenceTransformer('{MODEL_NAME}')33embeddings = model.encode(sentences)34print(embeddings)35```36 37 38 39## Evaluation Results40 41<!--- Describe how your model was evaluated -->42 43For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})44 45 46## Training47The model was trained with the parameters:48 49**DataLoader**:50 51`torch.utils.data.dataloader.DataLoader` of length 5093 with parameters:52```53{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}54```55 56**Loss**:57 58`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 59 60Parameters of the fit()-Method:61```62{63    "epochs": 3,64    "evaluation_steps": 0,65    "evaluator": "NoneType",66    "max_grad_norm": 1,67    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",68    "optimizer_params": {69        "lr": 1.7094555110821448e-0570    },71    "scheduler": "WarmupLinear",72    "steps_per_epoch": 15279,73    "warmup_steps": 1528,74    "weight_decay": 0.0175}76```77 78 79## Full Model Architecture80```81SentenceTransformer(82  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 83  (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})84  (2): Normalize()85)86```87 88## Citing & Authors89 90<!--- Describe where people can find more information -->