---
library_name: transformers
tags: ['bart-base','sentiment classification','tweeter','binary classification']
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
This is a fine-tune of `bart-base` to a sentiment classification dataset. 

```python

from transformers import BartTokenizer, AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained('mylonasc/bart-base-twitter-sent-ft-001')

import torch
_phrases = [
    'this is a great model! I really like it!', 
    'Do you call this a model? This is not even 1B parameters! Get outta here!',
    'Fine tuning transformers is very easy if you use all the right tools!',
    "John couldn't write two correct lines of code without ChatGPT if his life depended on it..."
]
toks = tokenizer(_phrases, return_tensors='pt', padding = 'longest')
with torch.no_grad():
    res = model(**toks)[0]
is_positive = res.softmax(1)[:,1]
is_positive


>> tensor([0.9994, 0.1362, 0.9995, 0.3840])
```

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Model type:** BART-model (full-transformer)
- **Language(s) (NLP):** English

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
Sentiment classification for english sentences. 

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

This is the output of a short technical implementation project to demonstrate fine-tuning models using the transformer library. 

* The model was trained on very short sentences (max 65 tokens)
* Some prelim. benchmarking showed that it does strongly out-perform a Zero-shot BART-large model on sentiment classification. 
* It is uncertain how the model will behave in other contexts

It is **not** recommended to use this model - use at your own risk! 

## How to Get Started with the Model



[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
A twitter sentiment classification dataset - not from Huggingface. 

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

5 epochs with 3e-5 learning rate, 256 batch size.
10% validation set held-out for early stopping (not included in the main set after training).

#### Preprocessing
* Some removal of rows with a log of special characters
* De-duplication was not necessary (max duplicates were 4, and it was single word tweets)



#### Hardware

1x RTX4090