ncoop57/codeformer-java

1---2pipeline_tag: sentence-similarity3tags:4- sentence-transformers5- feature-extraction6- sentence-similarity7---8 9# {MODEL_NAME}10 11This 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.12 13<!--- Describe your model here -->14 15## Usage (Sentence-Transformers)16 17Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:18 19```20pip install -U sentence-transformers21```22 23Then you can use the model like this:24 25```python26from sentence_transformers import SentenceTransformer27sentences = ["This is an example sentence", "Each sentence is converted"]28 29model = SentenceTransformer('{MODEL_NAME}')30embeddings = model.encode(sentences)31print(embeddings)32```33 34 35 36## Evaluation Results37 38<!--- Describe how your model was evaluated -->39 40For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})41 42 43## Training44The model was trained with the parameters:45 46**DataLoader**:47 48`torch.utils.data.dataloader.DataLoader` of length 14202 with parameters:49```50{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}51```52 53**Loss**:54 55`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:56  ```57  {'scale': 20.0, 'similarity_fct': 'cos_sim'}58  ```59 60Parameters of the fit()-Method:61```62{63    "callback": null,64    "epochs": 1,65    "evaluation_steps": 0,66    "evaluator": "NoneType",67    "max_grad_norm": 1,68    "optimizer_class": "<class 'transformers.optimization.AdamW'>",69    "optimizer_params": {70        "lr": 2e-0571    },72    "scheduler": "WarmupLinear",73    "steps_per_epoch": null,74    "warmup_steps": 10000,75    "weight_decay": 0.0176}77```78 79 80## Full Model Architecture81```82SentenceTransformer(83  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel 84  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})85  (2): Normalize()86)87```88 89## Citing & Authors90 91<!--- Describe where people can find more information -->