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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: sagemaker-distilbert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.921
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.921
verified: true
- name: Precision Macro
type: precision
value: 0.8870419502496194
verified: true
- name: Precision Micro
type: precision
value: 0.921
verified: true
- name: Precision Weighted
type: precision
value: 0.9208079974712109
verified: true
- name: Recall Macro
type: recall
value: 0.8688429370077566
verified: true
- name: Recall Micro
type: recall
value: 0.921
verified: true
- name: Recall Weighted
type: recall
value: 0.921
verified: true
- name: F1 Macro
type: f1
value: 0.87642650638535
verified: true
- name: F1 Micro
type: f1
value: 0.9209999999999999
verified: true
- name: F1 Weighted
type: f1
value: 0.9203938811554648
verified: true
- name: loss
type: loss
value: 0.23216551542282104
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. -->
# sagemaker-distilbert-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: 0.2322
- Accuracy: 0.921
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9306 | 1.0 | 500 | 0.2322 | 0.921 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|