README.md

---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- tweet_eval
model-index:
- name: MND_TweetEvalBert_model
  results: []
language:
- en
pipeline_tag: text-classification
metrics:
- accuracy
widget:
  - text: 'I loved Barbie and Oppenheimer'
    example_title: Barbenheimer
---

<!-- 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. -->

# MND_TweetEvalBert_model

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7241

## Model description
This is how to use the model with the transformer library to do a text classification task.
This model was trained and built for sentiment analysis with a text classification model architecture.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("barbieheimer/MND_TweetEvalBert_model")
model = AutoModelForSequenceClassification.from_pretrained("barbieheimer/MND_TweetEvalBert_model")

# We can now use the model in the pipeline.
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

# Get some text to fool around with for a basic test.
text = "I loved Oppenheimer and Barbie "

classifier(text) # Let's see if the model works on our example text.
```
```
[{'label': 'JOY', 'score': 0.9845513701438904}]
```
## Training Evalutation Results

```python
{'eval_loss': 0.7240552306175232,
 'eval_runtime': 3.7803,
 'eval_samples_per_second': 375.896,
 'eval_steps_per_second': 23.543,
 'epoch': 5.0}
```

## Overall Model Evaluation Results

```python
{'accuracy': {'confidence_interval': (0.783, 0.832),
  'standard_error': 0.01241992329458207,
  'score': 0.808},
 'total_time_in_seconds': 150.93268656500004,
 'samples_per_second': 6.625470087086432,
 'latency_in_seconds': 0.15093268656500003}
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results
```python
{'training_loss'=0.3821827131159165}

{'train_runtime': 174.1546, 'train_samples_per_second': 93.509,
'train_steps_per_second': 5.857, 'total_flos': 351397804992312.0,
'train_loss': 0.3821827131159165, 'epoch': 5.0}
```

```
Step: 500
{training loss: 0.607100}

Step: 1000
{training loss: 0.169000}
```
### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3