Instructions to use superspray/electra_large_discriminator_squad2_custom_dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superspray/electra_large_discriminator_squad2_custom_dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="superspray/electra_large_discriminator_squad2_custom_dataset")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("superspray/electra_large_discriminator_squad2_custom_dataset") model = AutoModelForQuestionAnswering.from_pretrained("superspray/electra_large_discriminator_squad2_custom_dataset") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Question & Answering Model for 'Save Your Minutes' from Dobby-AI
Electra_Large Discriminator fine-tuned on SQuAD2.0 and custom QA dataset
This model is ahotrod/electra_large_discriminator_squad2_512 trained on additional custom dataset as:
!python3 run_squad.py --model_type electra \
--model_name_or_path /content/electra_large_512 \
--do_lower_case \
--output_dir /content/model/\
--do_train \
--train_file $data_dir/additional_qa.json\
--version_2_with_negative \
--do_lower_case \
--num_train_epochs 3 \
--weight_decay 0.01 \
--learning_rate 3e-5 \
--max_grad_norm 0.5 \
--adam_epsilon 1e-6 \
--max_seq_length 512 \
--doc_stride 128 \
--threads 12 \
--logging_steps 50 \
--save_steps 1000 \
--overwrite_output_dir \
--per_gpu_train_batch_size 4
```
We used Google Colab for training the model,
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