nayan06/binary-classifier-conversion-intent-1.0

1---2pipeline_tag: sentence-similarity3tags:4- sentence-transformers5- feature-extraction6- sentence-similarity7- setfit classification8- binary_classification9 10---11 12 13this is a setfit classifier which can be used for conversion or other , binary classification14 15<!--- Describe your model here -->16 17## Usage (Sentence-Transformers)18 19Using this model becomes easy when you have SetFit installed, 20```21pip install setfit22```23 24Then you can use the model like this:25 26```python27from setfit import SetFitModel, SetFitTrainer28model = SetFitModel.from_pretrained("nayan06/binary-classifier-conversion-intent-1.0")29preds = model(["view details"])30```31 32 33 34 35## Training36The model was trained with the parameters:37 38**DataLoader**:39 40`torch.utils.data.dataloader.DataLoader` of length 573 with parameters:41```42{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}43```44 45**Loss**:46 47`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 48 49Parameters of the fit()-Method:50```51{52    "epochs": 10,53    "evaluation_steps": 0,54    "evaluator": "NoneType",55    "max_grad_norm": 1,56    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",57    "optimizer_params": {58        "lr": 2e-0559    },60    "scheduler": "WarmupLinear",61    "steps_per_epoch": 573,62    "warmup_steps": 58,63    "weight_decay": 0.0164}65```66 67 68## Full Model Architecture69```70SentenceTransformer(71  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 72  (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})73  (2): Normalize()74)75```76 77## Citing & Authors78 79<!--- Describe where people can find more information -->