Migrate model card from transformers-repo
Browse filesRead announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/valhalla/bart-large-finetuned-squadv1/README.md
README.md
ADDED
|
@@ -0,0 +1,67 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
datasets:
|
| 3 |
+
- squad
|
| 4 |
+
---
|
| 5 |
+
|
| 6 |
+
# BART-LARGE finetuned on SQuADv1
|
| 7 |
+
|
| 8 |
+
This is bart-large model finetuned on SQuADv1 dataset for question answering task
|
| 9 |
+
|
| 10 |
+
## Model details
|
| 11 |
+
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**.
|
| 12 |
+
BART is a seq2seq model intended for both NLG and NLU tasks.
|
| 13 |
+
|
| 14 |
+
To use BART for question answering tasks, we feed the complete document into the encoder and decoder, and use the top
|
| 15 |
+
hidden state of the decoder as a representation for each
|
| 16 |
+
word. This representation is used to classify the token. As given in the paper bart-large achives comparable to ROBERTa on SQuAD.
|
| 17 |
+
Another notable thing about BART is that it can handle sequences with upto 1024 tokens.
|
| 18 |
+
|
| 19 |
+
| Param | #Value |
|
| 20 |
+
|---------------------|--------|
|
| 21 |
+
| encoder layers | 12 |
|
| 22 |
+
| decoder layers | 12 |
|
| 23 |
+
| hidden size | 4096 |
|
| 24 |
+
| num attetion heads | 16 |
|
| 25 |
+
| on disk size | 1.63GB |
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
## Model training
|
| 29 |
+
This model was trained on google colab v100 GPU.
|
| 30 |
+
You can find the fine-tuning colab here
|
| 31 |
+
[](https://colab.research.google.com/drive/1I5cK1M_0dLaf5xoewh6swcm5nAInfwHy?usp=sharing).
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
## Results
|
| 35 |
+
The results are actually slightly worse than given in the paper.
|
| 36 |
+
In the paper the authors mentioned that bart-large achieves 88.8 EM and 94.6 F1
|
| 37 |
+
|
| 38 |
+
| Metric | #Value |
|
| 39 |
+
|--------|--------|
|
| 40 |
+
| EM | 86.8022|
|
| 41 |
+
| F1 | 92.7342|
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
## Model in Action 🚀
|
| 45 |
+
```python3
|
| 46 |
+
from transformers import BartTokenizer, BartForQuestionAnswering
|
| 47 |
+
import torch
|
| 48 |
+
|
| 49 |
+
tokenizer = BartTokenizer.from_pretrained('valhalla/bart-large-finetuned-squadv1')
|
| 50 |
+
model = BartForQuestionAnswering.from_pretrained('valhalla/bart-large-finetuned-squadv1')
|
| 51 |
+
|
| 52 |
+
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
|
| 53 |
+
encoding = tokenizer(question, text, return_tensors='pt')
|
| 54 |
+
input_ids = encoding['input_ids']
|
| 55 |
+
attention_mask = encoding['attention_mask']
|
| 56 |
+
|
| 57 |
+
start_scores, end_scores = model(input_ids, attention_mask=attention_mask, output_attentions=False)[:2]
|
| 58 |
+
|
| 59 |
+
all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
|
| 60 |
+
answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
|
| 61 |
+
answer = tokenizer.convert_tokens_to_ids(answer.split())
|
| 62 |
+
answer = tokenizer.decode(answer)
|
| 63 |
+
#answer => 'a nice puppet'
|
| 64 |
+
```
|
| 65 |
+
|
| 66 |
+
> Created with ❤️ by Suraj Patil [](https://github.com/patil-suraj/)
|
| 67 |
+
[](https://twitter.com/psuraj28)
|