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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8885
- name: F1
type: f1
value: 0.8818845305609924
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.892
verified: true
- name: Precision Macro
type: precision
value: 0.8923475194643138
verified: true
- name: Precision Micro
type: precision
value: 0.892
verified: true
- name: Precision Weighted
type: precision
value: 0.894495118514709
verified: true
- name: Recall Macro
type: recall
value: 0.768240931585822
verified: true
- name: Recall Micro
type: recall
value: 0.892
verified: true
- name: Recall Weighted
type: recall
value: 0.892
verified: true
- name: F1 Macro
type: f1
value: 0.7897026729904524
verified: true
- name: F1 Micro
type: f1
value: 0.892
verified: true
- name: F1 Weighted
type: f1
value: 0.8842367889371163
verified: true
- name: loss
type: loss
value: 0.34626322984695435
verified: true
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.8885
verified: true
- name: Precision Macro
type: precision
value: 0.8849064522901132
verified: true
- name: Precision Micro
type: precision
value: 0.8885
verified: true
- name: Precision Weighted
type: precision
value: 0.8922726271705158
verified: true
- name: Recall Macro
type: recall
value: 0.7854833401719518
verified: true
- name: Recall Micro
type: recall
value: 0.8885
verified: true
- name: Recall Weighted
type: recall
value: 0.8885
verified: true
- name: F1 Macro
type: f1
value: 0.8031492596189961
verified: true
- name: F1 Micro
type: f1
value: 0.8885
verified: true
- name: F1 Weighted
type: f1
value: 0.8818845305609924
verified: true
- name: loss
type: loss
value: 0.36373236775398254
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: 0.3663
- Accuracy: 0.8885
- F1: 0.8819
## 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: 128
- eval_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 125 | 0.5574 | 0.822 | 0.7956 |
| 0.7483 | 2.0 | 250 | 0.3663 | 0.8885 | 0.8819 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.1+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|