# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat")
model = AutoModelForQuestionAnswering.from_pretrained("andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat")Quick Links
bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat
This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
- Downloads last month
- 4
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat")