|
--- |
|
datasets: |
|
- squad |
|
--- |
|
|
|
# BART-LARGE finetuned on SQuADv1 |
|
|
|
This is bart-large model finetuned on SQuADv1 dataset for question answering task |
|
|
|
## Model details |
|
BART was propsed in the [paper](https://arxiv.org/abs/1910.13461) **BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**. |
|
BART is a seq2seq model intended for both NLG and NLU tasks. |
|
|
|
To use BART for question answering tasks, we feed the complete document into the encoder and decoder, and use the top |
|
hidden state of the decoder as a representation for each |
|
word. This representation is used to classify the token. As given in the paper bart-large achives comparable to ROBERTa on SQuAD. |
|
Another notable thing about BART is that it can handle sequences with upto 1024 tokens. |
|
|
|
| Param | #Value | |
|
|---------------------|--------| |
|
| encoder layers | 12 | |
|
| decoder layers | 12 | |
|
| hidden size | 4096 | |
|
| num attetion heads | 16 | |
|
| on disk size | 1.63GB | |
|
|
|
|
|
## Model training |
|
This model was trained on google colab v100 GPU. |
|
You can find the fine-tuning colab here |
|
[](https://colab.research.google.com/drive/1I5cK1M_0dLaf5xoewh6swcm5nAInfwHy?usp=sharing). |
|
|
|
|
|
## Results |
|
The results are actually slightly worse than given in the paper. |
|
In the paper the authors mentioned that bart-large achieves 88.8 EM and 94.6 F1 |
|
|
|
| Metric | #Value | |
|
|--------|--------| |
|
| EM | 86.8022| |
|
| F1 | 92.7342| |
|
|
|
|
|
## Model in Action 馃殌 |
|
```python3 |
|
from transformers import BartTokenizer, BartForQuestionAnswering |
|
import torch |
|
|
|
tokenizer = BartTokenizer.from_pretrained('valhalla/bart-large-finetuned-squadv1') |
|
model = BartForQuestionAnswering.from_pretrained('valhalla/bart-large-finetuned-squadv1') |
|
|
|
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" |
|
encoding = tokenizer(question, text, return_tensors='pt') |
|
input_ids = encoding['input_ids'] |
|
attention_mask = encoding['attention_mask'] |
|
|
|
start_scores, end_scores = model(input_ids, attention_mask=attention_mask, output_attentions=False)[:2] |
|
|
|
all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0]) |
|
answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1]) |
|
answer = tokenizer.convert_tokens_to_ids(answer.split()) |
|
answer = tokenizer.decode(answer) |
|
#answer => 'a nice puppet' |
|
``` |
|
|
|
> Created with 鉂わ笍 by Suraj Patil [](https://github.com/patil-suraj/) |
|
[](https://twitter.com/psuraj28) |
|
|
