swardiantara/message-problem-embedding

1---2library_name: sentence-transformers3pipeline_tag: sentence-similarity4tags:5- sentence-transformers6- feature-extraction7- sentence-similarity8 9---10 11# message-embedding12 13This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.14 15<!--- Describe your model here -->16 17## Usage (Sentence-Transformers)18 19Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:20 21```22pip install -U sentence-transformers23```24 25Then you can use the model like this:26 27```python28from sentence_transformers import SentenceTransformer29sentences = ["This is an example sentence", "Each sentence is converted"]30 31model = SentenceTransformer('message-embedding')32embeddings = model.encode(sentences)33print(embeddings)34```35 36 37 38## Evaluation Results39 40<!--- Describe how your model was evaluated -->41 42For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=message-embedding)43 44 45## Training46The model was trained with the parameters:47 48**DataLoader**:49 50`torch.utils.data.dataloader.DataLoader` of length 42852 with parameters:51```52{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}53```54 55**Loss**:56 57`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:58  ```59  {'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.3, 'size_average': True}60  ```61 62Parameters of the fit()-Method:63```64{65    "epochs": 3,66    "evaluation_steps": 0,67    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",68    "max_grad_norm": 1,69    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",70    "optimizer_params": {71        "lr": 2e-0572    },73    "scheduler": "WarmupLinear",74    "steps_per_epoch": null,75    "warmup_steps": 12855,76    "weight_decay": 0.0177}78```79 80 81## Full Model Architecture82```83SentenceTransformer(84  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 85  (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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})86  (2): Normalize()87)88```89 90## Citing & Authors91 92<!--- Describe where people can find more information -->