Minuva Models
Collection
Fast and light models for conversational data.
•
12 items
•
Updated
•
1
Text Classification GoEmotions
This model is a fined-tuned version of MiniLMv2-L6-H384 on the on the go_emotions dataset. The quantized version in ONNX format can be found here
Load the Model
from transformers import pipeline
pipe = pipeline(model='minuva/MiniLMv2-goemotions-v2', task='text-classification')
pipe("I am angry")
# [{'label': 'anger', 'score': 0.9722517132759094}]
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
Metrics (comparison with teacher model)
| Teacher (params) | Student (params) | Set | Score (teacher) | Score (student) |
|---|---|---|---|---|
| tasinhoque/text-classification-goemotions (355M) | MiniLMv2-goemotions-v2 (30M) | Validation | 0.514252 | 0.484898 |
| tasinhoque/text-classification-goemotions (355M) | MiniLMv2-goemotions-v2 (30M) | Test | 0.501937 | 0.486890 |
Deployment
Check out our fast-nlp-text-emotion repository for a FastAPI based server to easily deploy this model on CPU devices.
- Downloads last month
- 108
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.