metadata
pipeline_tag: text-classification
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- Setfit
language:
- en
library_name: sentence-transformers
{Setfit_youtube_comments}
This is a Setfit model: It maps sentences to a n dimensional dense vector space and can be used for classification of text into question or not_question class.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers and setfit installed:
pip install -U sentence-transformers
pip install setfit
Then you can use the model like this:
from setfit import SetFitModel
model = SetFitModel.from_pretrained("tushifire/setfit_youtube_comments_is_a_question")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
print(preds)
preds = model(["""what video do I watch that takes the html_output and insert it into the actual html page?""",
"Why does for loop end without a break statement"])
print(preds)
Training
The model was trained with the parameters:
DataLoader:
torch.utils.data.dataloader.DataLoader of length 80 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": 800,
"warmup_steps": 80,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, '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()
)