electra-small-finetuned-squadv1 model card (#5430)
* Create model card Create model card for electra-small-discriminator finetuned on SQUAD v1.1 * Set right model path in code example
This commit is contained in:
parent
43b7ad5df5
commit
e6eba8419c
|
@ -0,0 +1,87 @@
|
|||
---
|
||||
language: english
|
||||
---
|
||||
|
||||
# Electra small ⚡ + SQuAD v1 ❓
|
||||
|
||||
[Electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) fine-tuned on [SQUAD v1.1 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task.
|
||||
|
||||
## Details of the downstream task (Q&A) - Model 🧠
|
||||
|
||||
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
|
||||
|
||||
|
||||
## Details of the downstream task (Q&A) - Dataset 📚
|
||||
|
||||
**S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
|
||||
SQuAD v1.1 contains **100,000+** question-answer pairs on **500+** articles.
|
||||
|
||||
## Model training 🏋️
|
||||
|
||||
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
|
||||
|
||||
```bash
|
||||
python transformers/examples/question-answering/run_squad.py \
|
||||
--model_type electra \
|
||||
--model_name_or_path 'google/electra-small-discriminator' \
|
||||
--do_eval \
|
||||
--do_train \
|
||||
--do_lower_case \
|
||||
--train_file '/content/dataset/train-v1.1.json' \
|
||||
--predict_file '/content/dataset/dev-v1.1.json' \
|
||||
--per_gpu_train_batch_size 16 \
|
||||
--learning_rate 3e-5 \
|
||||
--num_train_epochs 10 \
|
||||
--max_seq_length 384 \
|
||||
--doc_stride 128 \
|
||||
--output_dir '/content/output' \
|
||||
--overwrite_output_dir \
|
||||
--save_steps 1000
|
||||
```
|
||||
|
||||
## Test set Results 🧾
|
||||
|
||||
| Metric | # Value |
|
||||
| ------ | --------- |
|
||||
| **EM** | **77.70** |
|
||||
| **F1** | **85.74** |
|
||||
| **Size**| **50 MB** |
|
||||
|
||||
Very good metrics for such a "small" model!
|
||||
|
||||
```json
|
||||
|
||||
{
|
||||
'exact': 77.70104068117313,
|
||||
'f1': 85.73991234187997,
|
||||
'total': 10570,
|
||||
'HasAns_exact': 77.70104068117313,
|
||||
'HasAns_f1': 85.73991234187997,
|
||||
'HasAns_total': 10570,
|
||||
'best_exact': 77.70104068117313,
|
||||
'best_exact_thresh': 0.0,
|
||||
'best_f1': 85.73991234187997,
|
||||
'best_f1_thresh': 0.0
|
||||
}
|
||||
```
|
||||
|
||||
### Model in action 🚀
|
||||
|
||||
Fast usage with **pipelines**:
|
||||
|
||||
```python
|
||||
from transformers import pipeline
|
||||
|
||||
QnA_pipeline = pipeline('question-answering', model='mrm8488/electra-small-finetuned-squadv1')
|
||||
QnA_pipeline({
|
||||
'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.',
|
||||
'question': 'What has been discovered by scientists from China ?'
|
||||
})
|
||||
|
||||
# Output:
|
||||
{'answer': 'A new strain of flu', 'end': 19, 'score': 0.7950334108113424, 'start': 0}
|
||||
```
|
||||
|
||||
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
|
||||
|
||||
> Made with <span style="color: #e25555;">♥</span> in Spain
|
Loading…
Reference in New Issue