
# 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: 0.2287
- Accuracy: 0.918
- F1: 0.9182

## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8478        | 1.0   | 250  | 0.3294          | 0.9015   | 0.8980 |
| 0.2616        | 2.0   | 500  | 0.2287          | 0.918    | 0.9182 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.918, "name": "Accuracy"}, {"type": "f1", "value": 0.9182094401352938, "name": "F1"}]}, {"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "default", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9185, "name": "Accuracy", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGFmYmNlNzU0NzNlMGU4NDI1ZjAyMzRjY2U4NzZkMjVkNmM5Zjk2ZGNmNjBiZmY0YjY1Zjg3MzViMmRlMmRiOSIsInZlcnNpb24iOjF9.7VJ4JGkOHZ7jp_hA9Jx0ToQ74OBp918a1OVZ3qpuv1ZV1qkPrCVW9_g72v0QjmICdlHvHrBwvKywdzv-It6RCg"}, {"type": "precision", "value": 0.8948630809230339, "name": "Precision Macro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDRhYjBjYzViMGY2MjE4OGU2OWZlYTUzNDljMjllYTAyMGI4Y2FhODQxOWU2N2NkNTYyOGJhZjA4MmFkOWFiOCIsInZlcnNpb24iOjF9.0rf2OHpdMViVl-vFQIE0g5qFmpvSfWa1Igs9Ala_T0foNk1rD4IR_bLDHqbU57HWDDYFKK2EKfV9KK19-pONBg"}, {"type": "precision", "value": 0.9185, "name": "Precision Micro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTM0YjhmZDVhYTlhZWQ3ZGQwOTRjNGI0NTU0OTFlZjFlMTE5ODQwY2E2ZTZhZmMxYjA5NDc0MzgxMjFkZjNmMyIsInZlcnNpb24iOjF9.n1LvyMO5EkZ5H7zkB533gP8w7FMpv8TxgaeaqiM-fAHmrMsF_-Dkc0X5tjI5_QQGU2aqXOHdThmWI1ohelJoDw"}, {"type": "precision", "value": 0.9190547804558933, "name": "Precision Weighted", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmM5NDVmMDcwZjVhYWIyNTI1Njk3M2Y4ZDg0N2Q5NzU2NTU3YmZlNjEzNjcyY2VmODhhMWY5MGExZjViMjMzYSIsInZlcnNpb24iOjF9.gAvnEt3NSkc5Mp0JhezC6pfsa2nXVcvD-3dfFcRy_F4S-iv8u-WjC2sj5S3ieYmw5zZlgFVLiWj3N9WclLceBg"}, {"type": "recall", "value": 0.860108882009274, "name": "Recall Macro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDQ3ZjM3NGM4NzVjYzVkMWYxNmMzYjM1ODVhOWMwODk2NmE3NjcwNDRhMmQ0YTQ1NzdkNTNkZTEwYTBhMmIyYyIsInZlcnNpb24iOjF9.niXajj933x2yuG_AorT3Yp7_60MgHy-eXkwpjp1ERCknWcxJ5BB38-tJdP9ambP3QeGJYtjPlXVeQLpaQ7rdAw"}, {"type": "recall", "value": 0.9185, "name": "Recall Micro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjE4OWM0OTVkMDllN2JiNjUxMWNlOWUzNTNkYmU3M2U1YzIyODBkNjk5YTBhMmFmMzM5Mzk1NjRkNjRmMjUxZSIsInZlcnNpb24iOjF9.S0di5PwvB-9NpPh6d1VOBUZOqIxVdyfPeUIc5NCTZ6-hc4NrWyAsrs_-3ybbhnws6ZqgQh8S-oCLPj142J0LCA"}, {"type": "recall", "value": 0.9185, "name": "Recall Weighted", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWI0Zjc5ZGIwMzdhMzRiYjgxYzcxZWVjMDczZTcxMWZkYTljOTI0MjVkOTU0MDdiNDYzMjkyNThmNmUwMWQxYSIsInZlcnNpb24iOjF9.fdOWpzsUjzuC_jL4Iy4AY-gloMO3_cuxwvFs-2ViJU4RLn7xnJNqdID5hyuoSlytpYyk8yf0J8tImddj_V4qBg"}, {"type": "f1", "value": 0.8727941247828231, "name": "F1 Macro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjQ1ZGEwOTMxYjAzNjgxYmMzZGM1ZjkwNmNiYzdmOWE3MGI5NzY5NjM3ZDljZTVmZWQ4YThlMTExYjE2MzkxNyIsInZlcnNpb24iOjF9.y4K4-ICKWoib_dtJkrTjPrrrWVQO4vMJ4OZeXu4yrCHBEwc5Pa-605oDLjujZcVI5Vn2lE3piUUJn_Ko_eRKBQ"}, {"type": "f1", "value": 0.9185, "name": "F1 Micro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjBjYjUzZTlkYzJjZDhkMjM4MjBlZWYwNjA4NTZlZjY2Njc0ZDgyZjYyNjU5ZmM0YzY3ODFlN2ZlMWRiZDZmYiIsInZlcnNpb24iOjF9.WXwc2VTkkUDPCY5JxnHFPduRa_iViuxS3MvNiH4Od2kRNnIYxlFY2wo1yT3UQukAnz69Uq6M_aSi6a7qnxt7Bg"}, {"type": "f1", "value": 0.9177368694234422, "name": "F1 Weighted", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGMxMzBjOTNhOWVmZDg0NjlmMmFhY2RmYzc0YzRlMTkyN2E4NTVmYzdkYWEwMDljY2U5ZmQ5YmM5ZjlhYWNlMiIsInZlcnNpb24iOjF9.XcschKnQYuy1KCgM-eTPJxHaTyj4iRkmdc8Pyxa3i1b_7a8FOr5vBUdijrnh1sEj4Cg08yrM5o59sGWRz_ZuDg"}, {"type": "loss", "value": 0.21989187598228455, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTY0MDUwNGUyYTA1NjIyZTkzMzc5ODI5ZTE2ZDY5M2I3NzM2ZTZhNTQxODY5ZGY4MmUzZGFmYTU3M2FmZTc1ZCIsInZlcnNpb24iOjF9.y7Ylg_yZ-pqRohxawrTNQU6DpVlVP7bBNwsoOvpzcPJncNR2CG94edcvi4F6w86EcDsPEm0ab4XK3elAAhC6Dw"}]}]}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer dataset:emotion license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-finetuned-emotion
=========================================


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


* Loss: 0.2287
* Accuracy: 0.918
* F1: 0.9182


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: 2


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6



### 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: 2 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #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: 2 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #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: 2### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6