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---
license: apache-2.0
tags:
- generated_from_trainer
- text-classification
- emotion
- pytorch
language:
- en
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-cased-emotion
results:
- task:
type: text-classification
name: text-classification
dataset:
name: emotion
type: emotion
config: default
split: validation
metrics:
- name: accuracy
type: accuracy
value: 0.9235
verified: true
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.9235
verified: true
- name: Precision Macro
type: precision
value: 0.89608475565062
verified: true
- name: Precision Micro
type: precision
value: 0.9235
verified: true
- name: Precision Weighted
type: precision
value: 0.9224273416855945
verified: true
- name: Recall Macro
type: recall
value: 0.8581097243584549
verified: true
- name: Recall Micro
type: recall
value: 0.9235
verified: true
- name: Recall Weighted
type: recall
value: 0.9235
verified: true
- name: F1 Macro
type: f1
value: 0.8746813002250796
verified: true
- name: F1 Micro
type: f1
value: 0.9235
verified: true
- name: F1 Weighted
type: f1
value: 0.9217456925724525
verified: true
- name: loss
type: loss
value: 0.32714536786079407
verified: true
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.938
verified: true
- name: Precision Macro
type: precision
value: 0.9281100797474869
verified: true
- name: Precision Micro
type: precision
value: 0.938
verified: true
- name: Precision Weighted
type: precision
value: 0.9376891512759605
verified: true
- name: Recall Macro
type: recall
value: 0.9029821552608664
verified: true
- name: Recall Micro
type: recall
value: 0.938
verified: true
- name: Recall Weighted
type: recall
value: 0.938
verified: true
- name: F1 Macro
type: f1
value: 0.9147207975135915
verified: true
- name: F1 Micro
type: f1
value: 0.938
verified: true
- name: F1 Weighted
type: f1
value: 0.9373403463117288
verified: true
- name: loss
type: loss
value: 0.23682540655136108
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-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](https://huggingface.co/distilbert-base-cased) on [emotion](https://huggingface.co/datasets/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
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