
<!-- 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. -->

# bertweet-base-finetuned-emotion

This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1737
- Accuracy: 0.929
- F1: 0.9296

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.9469        | 1.0   | 250  | 0.3643          | 0.895    | 0.8921 |
| 0.2807        | 2.0   | 500  | 0.2173          | 0.9245   | 0.9252 |
| 0.1749        | 3.0   | 750  | 0.1859          | 0.926    | 0.9266 |
| 0.1355        | 4.0   | 1000 | 0.1737          | 0.929    | 0.9296 |


### Framework versions

- Transformers 4.13.0
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bertweet-base-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.929, "name": "Accuracy"}, {"type": "f1", "value": 0.9295613935787139, "name": "F1"}]}, {"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "default", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.925, "name": "Accuracy", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZThkYWEwYTdjY2IwMmE4NmM2Mzc3ZTVkNTNmNWYwNGUxYTM5ZDA5ODEwMGQ1ZGU0ZmJmY2U1ZDhjYWRlZjU2NSIsInZlcnNpb24iOjF9.QJYOUR_EPrYzbZGBb1N27BSlTQIdvd1hmUfnfPJdTGGrNoQwXBUA4amVsWh1txV_YtO8hcCx-b3pTqzpdy1FAw"}, {"type": "precision", "value": 0.8722017563353339, "name": "Precision Macro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2Y3ZGM2NDk5ZTQyNTNjZDdmNjk5Y2IwNzkxNmU3MDM0YTljMTJjMzFmMTlkN2ZjN2NhZjNhYTVlMWY5NWFjNCIsInZlcnNpb24iOjF9.cBYScC_c6g1ECi3rj6HiRI3AMuoxg8wp7JKha0UKh1Q2qjzTr5ml8JAByPL0iu-Ix5BO2Bsx0fZNFhUS82LiCg"}, {"type": "precision", "value": 0.925, "name": "Precision Micro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjVjYjgwYjE2ZWUyM2Y5MzE0ODk4NDA5MGM2ODIxYTgxZDYyMTUxNzcwZWQ2MjZjZGYwODkyNzFkMjAxOTUzYyIsInZlcnNpb24iOjF9.phgA4BJcqp4ZUhecNeuGU8OAf6f_asN9Mf6JfFGd0cPORYltd_N4Wf6EXqu6z1ADqWeeibteEyIUwmmMEbjYBQ"}, {"type": "precision", "value": 0.9283646705517916, "name": "Precision Weighted", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjE4MDUyYjU4YTc4Mzk4YmIwMGIzZWEyYmU5ZDQ0MjRhZDQ4OGMwZjVmZmEyNDM5NzYyZTMzMTJiMmRkZTU4NiIsInZlcnNpb24iOjF9.LbYjoga-JSCzHZAF1fhm1CfuaSSI-ok0yXj3gtd4QTWY1TjzOHoMG3Q6zEGz84l6ASoHsvi9wjS7_EaSQLB4Dw"}, {"type": "recall", "value": 0.8982480793145559, "name": "Recall Macro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzFiY2JmYWRhYmE1YzA1MmRhODI0MGE1NTRiMWE3YmNjZWQ4OGExMDg3NmUzYmUzYTYyNTdkNjM1Y2M0ODJmMCIsInZlcnNpb24iOjF9.dAq2gloG0O-4z5Ng7RZkFO7e0og3wBQBmIDzic6onwjw83yaHPVfRd1e0j6mNhMUifOwPLEavnYkBYa9DVFqCw"}, {"type": "recall", "value": 0.925, "name": "Recall Micro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjg5Y2M2NWIwZmI4YjEyMmIzYjNjZmQyMzhlYjg2ZDRmY2U1M2I1NzQzYjRmMzYxYzJkNTI5MDJjMmY5ZDVmNyIsInZlcnNpb24iOjF9.Z5hmQBUsoKAgqTXk47aUDNKf5jJ0mXzY9TAgM9vG8I3pgCT465PEfM-TOKfG_YcPMLd3tkB8AdwDpmVnNj5QCw"}, {"type": "recall", "value": 0.925, "name": "Recall Weighted", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODIzMjY1YjgzYzAwNjk2NGI1ZjFhZTg4MGY1Mzg5NDhkM2EzY2JlMWM4MjZmNjg4ZmEyZDJmZTUwNDFkZmNiOCIsInZlcnNpb24iOjF9.S-9p04Lru9WTzm50mM5qGM4oA-TPgNw6uwxKr5AejU1iPKjyTDQvoumBs41T5OKL5zN_NyYXsFsCermSbirLAw"}, {"type": "f1", "value": 0.883488774573809, "name": "F1 Macro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGMyMjc3NTkxNDNjNTJiNmRmMTA4NzY0MTgyMDc3ZDE4N2RlMTY1YzU4OGQ1YmM1NzY2OGQ4Y2I0MzVhOGU3OSIsInZlcnNpb24iOjF9.D65sLHNZGjp15ra4i5ccYyOX705Xq-hftZjDb6kqE5X-jhzA5VLev6FirhnhyYLBQmA6Q9T1eDYHKkVZG4CcBg"}, {"type": "f1", "value": 0.925, "name": "F1 Micro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTNkMmUwNjYxNGE4M2E2MDA0NTI4ZjA1OTNkMWEwN2MzY2JmMWYxYThiYTZmM2MwZjM5YTIzMGIzMGI4ODJlZSIsInZlcnNpb24iOjF9.cB4WUQN_weyKdMZehH0ECaTcD9Jl1xzmrOzJZz27OJeCPjY0uW8O63HnJZ_LmBF2xqd7HDypT4s8hZBMT-6eDw"}, {"type": "f1", "value": 0.9259820821054494, "name": "F1 Weighted", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTY3NDhjNmMzYzM1MjIyY2FkYjI5YTQ1NTdmODhmMGVlNjc2ZjQ3MWZmZWEyMDQ1OGI1NDllZTBhM2VjYzg2MSIsInZlcnNpb24iOjF9.Akd8PVgc2tyin_TaOZV1bio_b00g3QmlHA-GWV3rMX13B1imDLuPAuP-HWIwgqg-umQUkJzcUQlTqbcQ06v0DQ"}, {"type": "loss", "value": 0.18158096075057983, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDZhODkzODQ2ZjYyMWQxYjEzNzRmMmQ0NjM3M2RiNDdlMTcwOGRhYjA0NWEwYTVjMmY0ZWY3NGQ3MzFhMTQ3ZSIsInZlcnNpb24iOjF9.jzv7qMmQuFmrsR3WoRAsCbrRJhNk0sfEcN07lCqhxUwYcO4rblVbBiePQtr0IDN067PbQmV6ES6W2cjHqvuHAA"}]}]}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer dataset:emotion model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-emotion #model-index #autotrain_compatible #endpoints_compatible #region-us 

bertweet-base-finetuned-emotion
===============================


This model is a fine-tuned version of vinai/bertweet-base on the emotion dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1737
* Accuracy: 0.929
* F1: 0.9296


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: 4


### Training results



### Framework versions


* Transformers 4.13.0
* Pytorch 1.11.0+cu113
* Datasets 1.16.1
* Tokenizers 0.10.3



### 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: 4 ### Training results ### Framework versions


* Transformers 4.13.0
* Pytorch 1.11.0+cu113
* Datasets 1.16.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-emotion #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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: 4 ### Training results ### Framework versions


* Transformers 4.13.0
* Pytorch 1.11.0+cu113
* Datasets 1.16.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-emotion #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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: 4### Training results### Framework versions


* Transformers 4.13.0
* Pytorch 1.11.0+cu113
* Datasets 1.16.1
* Tokenizers 0.10.3