# CITDA:

Fine-tuned `bert-base-uncased` on the `emotions` dataset

Demo Notebook: https://colab.research.google.com/drive/10ZCFvlf2UV3FjU4ymf4OoipQvqHbIItG?usp=sharing

## Packages

- Install `torch`
- Also, `pip install transformers datasets scikit-learn wandb seaborn python-dotenv`


## Train

1. Rename `.env.example` to `.env` and set an API key from [wandb](https://wandb.ai/authorize)
2. You can adjust model parameters in the `explainableai.py` file.
2. The model (`pytorch_model.bin`) is a based on the `bert-base-uncased` and already trained on the `emotions` dataset. 
To re-produce the training run `finetune-emotions.py`. You can change the base model, or the dataset by changing that file's code.



## Example

Run `example.py`

## Train
The model is already trained on `bert-base-uncased` with the [emotions dataset](https://huggingface.co/datasets/emotion). However, you can change parameters and re-fine-tune the model by running `finetune-emotions.py`.
{"license": "cc-by-nc-sa-4.0"}
transformers pytorch bert text-classification license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

# CITDA:

Fine-tuned 'bert-base-uncased' on the 'emotions' dataset

Demo Notebook: URL

## Packages

- Install 'torch'
- Also, 'pip install transformers datasets scikit-learn wandb seaborn python-dotenv'


## Train

1. Rename '.env.example' to '.env' and set an API key from wandb
2. You can adjust model parameters in the 'URL' file.
2. The model ('pytorch_model.bin') is a based on the 'bert-base-uncased' and already trained on the 'emotions' dataset. 
To re-produce the training run 'URL'. You can change the base model, or the dataset by changing that file's code.



## Example

Run 'URL'

## Train
The model is already trained on 'bert-base-uncased' with the emotions dataset. However, you can change parameters and re-fine-tune the model by running 'URL'.
# CITDA:

Fine-tuned 'bert-base-uncased' on the 'emotions' dataset

Demo Notebook: URL ## Packages

- Install 'torch'
- Also, 'pip install transformers datasets scikit-learn wandb seaborn python-dotenv' ## Train

1. Rename '.env.example' to '.env' and set an API key from wandb
2. You can adjust model parameters in the 'URL' file.
2. The model ('pytorch_model.bin') is a based on the 'bert-base-uncased' and already trained on the 'emotions' dataset. 
To re-produce the training run 'URL'. You can change the base model, or the dataset by changing that file's code. ## Example

Run 'URL' ## Train
The model is already trained on 'bert-base-uncased' with the emotions dataset. However, you can change parameters and re-fine-tune the model by running 'URL'.
TAGS
#transformers #pytorch #bert #text-classification #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # CITDA:

Fine-tuned 'bert-base-uncased' on the 'emotions' dataset

Demo Notebook: URL ## Packages

- Install 'torch'
- Also, 'pip install transformers datasets scikit-learn wandb seaborn python-dotenv' ## Train

1. Rename '.env.example' to '.env' and set an API key from wandb
2. You can adjust model parameters in the 'URL' file.
2. The model ('pytorch_model.bin') is a based on the 'bert-base-uncased' and already trained on the 'emotions' dataset. 
To re-produce the training run 'URL'. You can change the base model, or the dataset by changing that file's code. ## Example

Run 'URL' ## Train
The model is already trained on 'bert-base-uncased' with the emotions dataset. However, you can change parameters and re-fine-tune the model by running 'URL'.
passage: TAGS
#transformers #pytorch #bert #text-classification #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# CITDA:

Fine-tuned 'bert-base-uncased' on the 'emotions' dataset

Demo Notebook: URL## Packages

- Install 'torch'
- Also, 'pip install transformers datasets scikit-learn wandb seaborn python-dotenv'## Train

1. Rename '.env.example' to '.env' and set an API key from wandb
2. You can adjust model parameters in the 'URL' file.
2. The model ('pytorch_model.bin') is a based on the 'bert-base-uncased' and already trained on the 'emotions' dataset. 
To re-produce the training run 'URL'. You can change the base model, or the dataset by changing that file's code.## Example

Run 'URL'## Train
The model is already trained on 'bert-base-uncased' with the emotions dataset. However, you can change parameters and re-fine-tune the model by running 'URL'.