# wangchanberta-base-wiki-20210520-news-spm-finetune-qa

Finetuning `airesearchth/wangchanberta-base-wiki-20210520-news-spm` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).

Run with:
```
export MODEL_NAME=airesearchth/wangchanberta-base-wiki-20210520-news-spm
CUDA_LAUNCH_BLOCKING=1 python train_question_answering_lm_finetuning.py \
  --model_name $MODEL_NAME \
  --dataset_name chimera_qa \
  --output_dir $MODEL_NAME-finetune-chimera_qa-model \
  --log_dir $MODEL_NAME-finetune-chimera_qa-log \
  --model_max_length 400 \
  --pad_on_right \
  --fp16
```

{"widget": [{"text": "\u0e2a\u0e27\u0e19\u0e01\u0e38\u0e2b\u0e25\u0e32\u0e1a\u0e40\u0e1b\u0e47\u0e19\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2d\u0e30\u0e44\u0e23", "context": "\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2a\u0e27\u0e19\u0e01\u0e38\u0e2b\u0e25\u0e32\u0e1a\u0e27\u0e34\u0e17\u0e22\u0e32\u0e25\u0e31\u0e22 (Suankularb Wittayalai School) (\u0e2d\u0e31\u0e01\u0e29\u0e23\u0e22\u0e48\u0e2d : \u0e2a.\u0e01. / S.K.) \u0e40\u0e1b\u0e47\u0e19\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e0a\u0e32\u0e22\u0e25\u0e49\u0e27\u0e19 \u0e23\u0e30\u0e14\u0e31\u0e1a\u0e0a\u0e31\u0e49\u0e19\u0e21\u0e31\u0e18\u0e22\u0e21\u0e28\u0e36\u0e01\u0e29\u0e32\u0e02\u0e19\u0e32\u0e14\u0e43\u0e2b\u0e0d\u0e48\u0e1e\u0e34\u0e40\u0e28\u0e29 \u0e2a\u0e31\u0e07\u0e01\u0e31\u0e14\u0e2a\u0e33\u0e19\u0e31\u0e01\u0e07\u0e32\u0e19\u0e40\u0e02\u0e15\u0e1e\u0e37\u0e49\u0e19\u0e17\u0e35\u0e48\u0e01\u0e32\u0e23\u0e28\u0e36\u0e01\u0e29\u0e32\u0e21\u0e31\u0e18\u0e22\u0e21\u0e28\u0e36\u0e01\u0e29\u0e32\u0e40\u0e02\u0e15 1 \u0e2a\u0e33\u0e19\u0e31\u0e01\u0e07\u0e32\u0e19\u0e04\u0e13\u0e30\u0e01\u0e23\u0e23\u0e21\u0e01\u0e32\u0e23\u0e01\u0e32\u0e23\u0e28\u0e36\u0e01\u0e29\u0e32\u0e02\u0e31\u0e49\u0e19\u0e1e\u0e37\u0e49\u0e19\u0e10\u0e32\u0e19 (\u0e0a\u0e37\u0e48\u0e2d\u0e40\u0e14\u0e34\u0e21: \u0e01\u0e23\u0e21\u0e2a\u0e32\u0e21\u0e31\u0e0d\u0e28\u0e36\u0e01\u0e29\u0e32) \u0e01\u0e23\u0e30\u0e17\u0e23\u0e27\u0e07\u0e28\u0e36\u0e01\u0e29\u0e32\u0e18\u0e34\u0e01\u0e32\u0e23 \u0e01\u0e48\u0e2d\u0e15\u0e31\u0e49\u0e07\u0e42\u0e14\u0e22 \u0e1e\u0e23\u0e30\u0e1a\u0e32\u0e17\u0e2a\u0e21\u0e40\u0e14\u0e47\u0e08\u0e1e\u0e23\u0e30\u0e08\u0e38\u0e25\u0e08\u0e2d\u0e21\u0e40\u0e01\u0e25\u0e49\u0e32\u0e40\u0e08\u0e49\u0e32\u0e2d\u0e22\u0e39\u0e48\u0e2b\u0e31\u0e27 \u0e44\u0e14\u0e49\u0e23\u0e31\u0e1a\u0e01\u0e32\u0e23\u0e2a\u0e16\u0e32\u0e1b\u0e19\u0e32\u0e02\u0e36\u0e49\u0e19\u0e43\u0e19\u0e27\u0e31\u0e19\u0e17\u0e35\u0e48 8 \u0e21\u0e35\u0e19\u0e32\u0e04\u0e21 \u0e1e.\u0e28. 2424 (\u0e02\u0e13\u0e30\u0e19\u0e31\u0e49\u0e19\u0e19\u0e31\u0e1a\u0e27\u0e31\u0e19\u0e17\u0e35\u0e48 1 \u0e40\u0e21\u0e29\u0e32\u0e22\u0e19 \u0e40\u0e1b\u0e47\u0e19\u0e27\u0e31\u0e19\u0e02\u0e36\u0e49\u0e19\u0e1b\u0e35\u0e43\u0e2b\u0e21\u0e48 \u0e40\u0e21\u0e37\u0e48\u0e2d\u0e19\u0e31\u0e1a\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e2a\u0e32\u0e01\u0e25\u0e16\u0e37\u0e2d\u0e40\u0e1b\u0e47\u0e19 \u0e1e.\u0e28. 2425) \u0e42\u0e14\u0e22\u0e40\u0e1b\u0e47\u0e19\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e23\u0e31\u0e10\u0e1a\u0e32\u0e25\u0e41\u0e2b\u0e48\u0e07\u0e41\u0e23\u0e01\u0e02\u0e2d\u0e07\u0e1b\u0e23\u0e30\u0e40\u0e17\u0e28\u0e44\u0e17\u0e22"}]}
transformers pytorch camembert question-answering endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us 

# wangchanberta-base-wiki-20210520-news-spm-finetune-qa

Finetuning 'airesearchth/wangchanberta-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.
Trained with thai2transformers.

Run with:


# wangchanberta-base-wiki-20210520-news-spm-finetune-qa

Finetuning 'airesearchth/wangchanberta-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.
Trained with thai2transformers.

Run with:
TAGS
#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us 
 # wangchanberta-base-wiki-20210520-news-spm-finetune-qa

Finetuning 'airesearchth/wangchanberta-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.
Trained with thai2transformers.

Run with:
passage: TAGS
#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us 
# wangchanberta-base-wiki-20210520-news-spm-finetune-qa

Finetuning 'airesearchth/wangchanberta-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.
Trained with thai2transformers.

Run with: