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# bert-finetuned-AAVE-PoS

This model is a version of [bert-base-cased](https://huggingface.co/bert-base-cased) fine-tuned on a [dataset](https://bitbucket.org/soegaard/aave-pos16/src/master/data) of African American Vernacular English (AAVE) which was published alongside [Jørgensen et al. 2016](https://aclanthology.org/N16-1130.pdf).
It achieves the following results on the evaluation set:
- Loss: 0.2582
- Precision: 0.8632
- Recall: 0.8730
- F1: 0.8681
- Accuracy: 0.9356

## Model description

More information needed

## Intended uses & limitations

This model is intended to help close the gap in part-of-speech tagging performance between Standard American English (SAE) and African American English (AAVE) which differ liguistically in many [well-documented](http://www.johnrickford.com/portals/45/documents/papers/Rickford-1999e-Phonological-and-Grammatical-Features-of-AAVE.pdf) ways. It was fine-tuned on data gathered from Twitter, and is thus ingrained with what linguists call 'register bias'. 

## Training and evaluation data

Code hosted at [GitHub](https://github.com/DrewGalbraith/AAE-PoS/tree/main).

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3 (this amount of data overfits on 3+)

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 223  | 0.2982          | 0.8196    | 0.8350 | 0.8272 | 0.9216   |
| No log        | 2.0   | 446  | 0.2625          | 0.8599    | 0.8680 | 0.8640 | 0.9326   |
| 0.4647        | 3.0   | 669  | 0.2582          | 0.8632    | 0.8730 | 0.8681 | 0.9356   |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.13.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "pipeline_tag": "token-classification", "model-index": [{"name": "bert-finetuned-ner", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-finetuned-AAVE-PoS
=======================


This model is a version of bert-base-cased fine-tuned on a dataset of African American Vernacular English (AAVE) which was published alongside Jørgensen et al. 2016.
It achieves the following results on the evaluation set:


* Loss: 0.2582
* Precision: 0.8632
* Recall: 0.8730
* F1: 0.8681
* Accuracy: 0.9356


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


This model is intended to help close the gap in part-of-speech tagging performance between Standard American English (SAE) and African American English (AAVE) which differ liguistically in many well-documented ways. It was fine-tuned on data gathered from Twitter, and is thus ingrained with what linguists call 'register bias'.


Training and evaluation data
----------------------------


Code hosted at GitHub.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 (this amount of data overfits on 3+)


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 1.13.1+cpu
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 (this amount of data overfits on 3+) ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.13.1+cpu
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 (this amount of data overfits on 3+) ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.13.1+cpu
* Datasets 2.12.0
* Tokenizers 0.13.3