Downloads · 30 days
81
7% of all-time downloads
distilbert-base-cased-emotion
morenolq/distilbert-base-cased-emotion
distilbert-base-cased-emotion is a text classification model from morenolq. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
1.1K
Public
Parameters
65.8M
5 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin263 MB · 50%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Sep 19, 2022
- Updated
- Nov 30, 2024
- SHA
- 95d44abc
Try a prompt
Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
distilbert-base-cased-emotion
Training: The model has been trained using the script provided in the following repository https://github.com/MorenoLaQuatra/transformers-tasks-templates
This model is a fine-tuned version of distilbert-base-cased on emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.3272
- Accuracy: 0.9235
- F1: 0.9217
- Precision: 0.9224
- Recall: 0.9235
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2776 | 1.0 | 500 | 0.2954 | 0.9 | 0.8957 | 0.9031 | 0.9 |
| 0.1887 | 2.0 | 1000 | 0.1716 | 0.934 | 0.9344 | 0.9370 | 0.934 |
| 0.119 | 3.0 | 1500 | 0.1614 | 0.9345 | 0.9342 | 0.9377 | 0.9345 |
| 0.1001 | 4.0 | 2000 | 0.2018 | 0.936 | 0.9353 | 0.9359 | 0.936 |
| 0.0704 | 5.0 | 2500 | 0.1925 | 0.935 | 0.9349 | 0.9354 | 0.935 |
| 0.0471 | 6.0 | 3000 | 0.2369 | 0.938 | 0.9373 | 0.9377 | 0.938 |
| 0.0322 | 7.0 | 3500 | 0.2693 | 0.938 | 0.9382 | 0.9392 | 0.938 |
| 0.0137 | 8.0 | 4000 | 0.2926 | 0.937 | 0.9371 | 0.9372 | 0.937 |
| 0.0099 | 9.0 | 4500 | 0.2964 | 0.9365 | 0.9362 | 0.9362 | 0.9365 |
| 0.0114 | 10.0 | 5000 | 0.3044 | 0.935 | 0.9349 | 0.9350 | 0.935 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Languages
- en
- Created
- Sep 19, 2022
- Updated
- Nov 30, 2024