AISE-TUDelft/python-summary-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<!--- Describe your model here -->15 16## Usage (Sentence-Transformers)17 18Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:19 20```21pip install -U sentence-transformers22```23 24Then you can use the model like this:25 26```python27from sentence_transformers import SentenceTransformer28sentences = ["This is an example sentence", "Each sentence is converted"]29 30model = SentenceTransformer('{MODEL_NAME}')31embeddings = model.encode(sentences)32print(embeddings)33```34 35 36 37## Evaluation Results38 39<!--- Describe how your model was evaluated -->40 41For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})42 43 44## Training45The model was trained with the parameters:46 47**DataLoader**:48 49`torch.utils.data.dataloader.DataLoader` of length 5098 with parameters:50```51{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}52```53 54**Loss**:55 56`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 57 58Parameters of the fit()-Method:59```60{61    "epochs": 3,62    "evaluation_steps": 0,63    "evaluator": "NoneType",64    "max_grad_norm": 1,65    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",66    "optimizer_params": {67        "lr": 1.7094555110821448e-0568    },69    "scheduler": "WarmupLinear",70    "steps_per_epoch": 15294,71    "warmup_steps": 1530,72    "weight_decay": 0.0173}74```75 76 77## Full Model Architecture78```79SentenceTransformer(80  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 81  (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})82  (2): Normalize()83)84```85 86## Citing & Authors87 88<!--- Describe where people can find more information -->