
# bart-base for Extractive QA

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset.

## Overview
**Language model:** bart-base  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** SQuAD 2.0  
**Eval data:** SQuAD 2.0  
**Infrastructure**: 1x NVIDIA 3070 


## Model Usage
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "sjrhuschlee/bart-base-squad2"
# a) Using pipelines
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
qa_input = {
'question': 'Where do I live?',
'context': 'My name is Sarah and I live in London'
}
res = nlp(qa_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

## Metrics

```bash
# Squad v2
{
    "eval_HasAns_exact": 76.45074224021593,
    "eval_HasAns_f1": 82.88605283171232,
    "eval_HasAns_total": 5928,
    "eval_NoAns_exact": 74.01177460050462,
    "eval_NoAns_f1": 74.01177460050462,
    "eval_NoAns_total": 5945,
    "eval_best_exact": 75.23793481007327,
    "eval_best_exact_thresh": 0.0,
    "eval_best_f1": 78.45098300230696,
    "eval_best_f1_thresh": 0.0,
    "eval_exact": 75.22951233892024,
    "eval_f1": 78.44256053115387,
    "eval_runtime": 131.875,
    "eval_samples": 11955,
    "eval_samples_per_second": 90.654,
    "eval_steps_per_second": 3.784,
    "eval_total": 11873
}

# Squad
{
    "eval_exact_match": 83.40586565752129,
    "eval_f1": 90.37706849113668,
    "eval_runtime": 117.2093,
    "eval_samples": 10619,
    "eval_samples_per_second": 90.599,
    "eval_steps_per_second": 3.78
}
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- max_seq_length 512
- doc_stride 128
- learning_rate: 2e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- optimizer: Adam8Bit with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
- gradient_checkpointing: True
- tf32: True


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "mit", "tags": ["bart", "question-answering", "squad", "squad_v2"], "datasets": ["squad_v2", "squad"], "base_model": "facebook/bart-base", "model-index": [{"name": "sjrhuschlee/bart-base-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 75.223, "name": "Exact Match"}, {"type": "f1", "value": 78.443, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 83.406, "name": "Exact Match"}, {"type": "f1", "value": 90.377, "name": "F1"}]}]}]}
transformers pytorch safetensors bart question-answering squad squad_v2 en dataset:squad_v2 dataset:squad base_model:facebook/bart-base license:mit model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bart #question-answering #squad #squad_v2 #en #dataset-squad_v2 #dataset-squad #base_model-facebook/bart-base #license-mit #model-index #endpoints_compatible #region-us 


# bart-base for Extractive QA

This model is a fine-tuned version of facebook/bart-base on the SQuAD2.0 dataset.

## Overview
Language model: bart-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Infrastructure: 1x NVIDIA 3070 


## Model Usage


## Metrics



## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- max_seq_length 512
- doc_stride 128
- learning_rate: 2e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- optimizer: Adam8Bit with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
- gradient_checkpointing: True
- tf32: True


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
# bart-base for Extractive QA

This model is a fine-tuned version of facebook/bart-base on the SQuAD2.0 dataset. ## Overview
Language model: bart-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Infrastructure: 1x NVIDIA 3070 ## Model Usage ## Metrics ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- max_seq_length 512
- doc_stride 128
- learning_rate: 2e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- optimizer: Adam8Bit with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
- gradient_checkpointing: True
- tf32: True ### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #bart #question-answering #squad #squad_v2 #en #dataset-squad_v2 #dataset-squad #base_model-facebook/bart-base #license-mit #model-index #endpoints_compatible #region-us 
 # bart-base for Extractive QA

This model is a fine-tuned version of facebook/bart-base on the SQuAD2.0 dataset. ## Overview
Language model: bart-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Infrastructure: 1x NVIDIA 3070 ## Model Usage ## Metrics ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- max_seq_length 512
- doc_stride 128
- learning_rate: 2e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- optimizer: Adam8Bit with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
- gradient_checkpointing: True
- tf32: True ### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3