# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Chetna19/chtn_bert_qa_model")
model = AutoModelForQuestionAnswering.from_pretrained("Chetna19/chtn_bert_qa_model", device_map="auto")Quick Links
chtn_bert_qa_model
This model is a fine-tuned version of deepset/bert-base-cased-squad2 on the subjqa dataset. It achieves the following results on the evaluation set:
- Loss: 3.2773
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 32 | 2.8350 |
| No log | 2.0 | 64 | 2.8860 |
| No log | 3.0 | 96 | 3.1081 |
| No log | 4.0 | 128 | 3.2405 |
| No log | 5.0 | 160 | 3.2773 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
- Downloads last month
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# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Chetna19/chtn_bert_qa_model")