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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- type: accuracy
value: 0.9275
name: Accuracy
- type: f1
value: 0.9273822408882375
name: F1
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- type: accuracy
value: 0.919
name: Accuracy
verified: true
- type: precision
value: 0.8882001804445858
name: Precision Macro
verified: true
- type: precision
value: 0.919
name: Precision Micro
verified: true
- type: precision
value: 0.9194695149914663
name: Precision Weighted
verified: true
- type: recall
value: 0.857858142469294
name: Recall Macro
verified: true
- type: recall
value: 0.919
name: Recall Micro
verified: true
- type: recall
value: 0.919
name: Recall Weighted
verified: true
- type: f1
value: 0.8684381937860847
name: F1 Macro
verified: true
- type: f1
value: 0.919
name: F1 Micro
verified: true
- type: f1
value: 0.9182406234430719
name: F1 Weighted
verified: true
- type: loss
value: 0.21632428467273712
name: loss
verified: true
---
<!-- 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-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2237
- Accuracy: 0.9275
- F1: 0.9274
## 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: 2e-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
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8643 | 1.0 | 250 | 0.3324 | 0.9065 | 0.9025 |
| 0.2589 | 2.0 | 500 | 0.2237 | 0.9275 | 0.9274 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|