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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- type: precision
value: 0.7262254187805659
name: Precision
- type: recall
value: 0.725549671319356
name: Recall
- task:
type: text-classification
name: Text Classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: test
metrics:
- type: accuracy
value: 0.7825475017593244
name: Accuracy
verified: true
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- type: f1
value: 0.7373078325463487
name: F1 Macro
verified: true
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- type: f1
value: 0.7825475017593244
name: F1 Micro
verified: true
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- type: f1
value: 0.7800585587569513
name: F1 Weighted
verified: true
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- type: precision
value: 0.757828822693908
name: Precision Macro
verified: true
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- type: precision
value: 0.7825475017593244
name: Precision Micro
verified: true
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- type: precision
value: 0.7817085293090495
name: Precision Weighted
verified: true
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- type: recall
value: 0.7268372321658545
name: Recall Macro
verified: true
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- type: recall
value: 0.7825475017593244
name: Recall Micro
verified: true
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- type: recall
value: 0.7825475017593244
name: Recall Weighted
verified: true
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- type: loss
value: 0.8246609568595886
name: loss
verified: true
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---
<!-- 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. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1670
- Precision: 0.7262
- Recall: 0.7255
- Fscore: 0.7253
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8561 | 1.0 | 815 | 0.7844 | 0.7575 | 0.6081 | 0.6253 |
| 0.5337 | 2.0 | 1630 | 0.9080 | 0.7567 | 0.7236 | 0.7325 |
| 0.2573 | 3.0 | 2445 | 1.1670 | 0.7262 | 0.7255 | 0.7253 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|