![florentgbelidji's picture](https://cdn-avatars.huggingface.co/v1/production/uploads/620a77b7dbba8fc1fbb8bdb4/ZRW2pH9Iawj700OyLpJl8.png)
florentgbelidji
HF staff
Add new SentenceTransformer model trained with SetFit on tweet_eval, emotion
63f01ba
pipeline_tag: sentence-similarity | |
tags: | |
- sentence-transformers | |
- feature-extraction | |
- sentence-similarity | |
# florentgbelidji/setfit_emotion | |
This 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. | |
<!--- Describe your model here --> | |
## Usage (Sentence-Transformers) | |
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: | |
``` | |
pip install -U sentence-transformers | |
``` | |
Then you can use the model like this: | |
```python | |
from sentence_transformers import SentenceTransformer | |
sentences = ["This is an example sentence", "Each sentence is converted"] | |
model = SentenceTransformer('florentgbelidji/setfit_emotion') | |
embeddings = model.encode(sentences) | |
print(embeddings) | |
``` | |
## Evaluation Results | |
<!--- Describe how your model was evaluated --> | |
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=florentgbelidji/setfit_emotion) | |
## Training | |
The model was trained with the parameters: | |
**DataLoader**: | |
`torch.utils.data.dataloader.DataLoader` of length 203 with parameters: | |
``` | |
{'batch_size': 16, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} | |
``` | |
**Loss**: | |
`sentence_transformers.losses.BatchHardTripletLoss.BatchHardTripletLoss` | |
Parameters of the fit()-Method: | |
``` | |
{ | |
"epochs": 1, | |
"evaluation_steps": 0, | |
"evaluator": "NoneType", | |
"max_grad_norm": 1, | |
"optimizer_class": "<class 'transformers.optimization.AdamW'>", | |
"optimizer_params": { | |
"lr": 2e-05 | |
}, | |
"scheduler": "WarmupLinear", | |
"steps_per_epoch": 4060, | |
"warmup_steps": 406, | |
"weight_decay": 0.01 | |
} | |
``` | |
## Full Model Architecture | |
``` | |
SentenceTransformer( | |
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel | |
(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}) | |
(2): Normalize() | |
) | |
``` | |
## Citing & Authors | |
<!--- Describe where people can find more information --> |