nayan06/binary-classifier-conversion-intent-1.0

this is a setfit classifier which can be used for conversion or other , binary classification

Usage (Sentence-Transformers)

Using this model becomes easy when you have SetFit installed,

pip install setfit

Then you can use the model like this:

from setfit import SetFitModel, SetFitTrainer
model = SetFitModel.from_pretrained("nayan06/binary-classifier-conversion-intent-1.0")
preds = model(["view details"])

Training

The model was trained with the parameters:

DataLoader:

torch.utils.data.dataloader.DataLoader of length 573 with parameters:

{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss:

sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss

Parameters of the fit()-Method:

{
    "epochs": 10,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": 573,
    "warmup_steps": 58,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
  (2): Normalize()
)

Citing & Authors