megagonlabs/subjqa
Updated • 1.16k • 16
How to use Chetna19/bert_qa_model_electronics_1 with Transformers:
# 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/bert_qa_model_electronics_1") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Chetna19/bert_qa_model_electronics_1")
model = AutoModelForQuestionAnswering.from_pretrained("Chetna19/bert_qa_model_electronics_1", device_map="auto")This model is a fine-tuned version of bert-large-uncased-whole-word-masking-finetuned-squad on the subjqa dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 32 | 11.5214 |
| No log | 2.0 | 64 | 11.1691 |
| No log | 3.0 | 96 | 11.6369 |
| No log | 4.0 | 128 | 11.7854 |
| No log | 5.0 | 160 | 11.8500 |