Back to all models
fill-mask mask_token: [MASK]
Query this model
🔥 This model is currently loaded and running on the Inference API. ⚠️ This model could not be loaded by the inference API. ⚠️ This model can be loaded on the Inference API on-demand.
JSON Output
API endpoint  

⚡️ Upgrade your account to access the Inference API

							$
							curl -X POST \
-H "Authorization: Bearer YOUR_ORG_OR_USER_API_TOKEN" \
-H "Content-Type: application/json" \
-d '"json encoded string"' \
https://api-inference.huggingface.co/models/aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616
Share Copied link to clipboard

Monthly model downloads

aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616 aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616
N/a downloads
last 30 days

pytorch

tf

Contributed by

aodiniz Adriano Orsoni Diniz
35 models

How to use this model directly from the 🤗/transformers library:

			
Copy to clipboard
from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616") model = AutoModelWithLMHead.from_pretrained("aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616")

BERT L-10 H-512 fine-tuned on MLM (CORD-19 2020/06/16)

BERT model with 10 Transformer layers and hidden embedding of size 512, referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models, fine-tuned for MLM on CORD-19 dataset (as released on 2020/06/16).

Training the model

python run_language_modeling.py
    --model_type bert
    --model_name_or_path google/bert_uncased_L-10_H-512_A-8
    --do_train
    --train_data_file {cord19-200616-dataset}
    --mlm
    --mlm_probability 0.2
    --line_by_line
    --block_size 512
    --per_device_train_batch_size 10
    --learning_rate 3e-5
    --num_train_epochs 2
    --output_dir bert_uncased_L-10_H-512_A-8_cord19-200616