Instructions to use Souvik123/bankstatementmodelver2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Souvik123/bankstatementmodelver2 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="Souvik123/bankstatementmodelver2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Souvik123/bankstatementmodelver2") model = AutoModelForQuestionAnswering.from_pretrained("Souvik123/bankstatementmodelver2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Souvik123/bankstatementmodelver2: direct link, hf CLI and curl.
- Browser
- Download file 1.82 kB
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https://huggingface.co/Souvik123/bankstatementmodelver2/resolve/main/README.md
- Command line
-
hf download hf://Souvik123/bankstatementmodelver2/README.md
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curl -L -o README.md https://huggingface.co/Souvik123/bankstatementmodelver2/resolve/main/README.md
1.82 kB
| license: apache-2.0 | |
| base_model: distilbert-base-uncased-distilled-squad | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: bankstatementmodelver2 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # bankstatementmodelver2 | |
| This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 5.9506 | |
| ## 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: 0.01 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 11 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 5.9985 | 1.0 | 981 | 5.9506 | | |
| | 5.9666 | 2.0 | 1962 | 5.9506 | | |
| | 5.9623 | 3.0 | 2943 | 5.9506 | | |
| | 5.9592 | 4.0 | 3924 | 5.9506 | | |
| | 5.9577 | 5.0 | 4905 | 5.9506 | | |
| | 5.956 | 6.0 | 5886 | 5.9506 | | |
| | 5.9565 | 7.0 | 6867 | 5.9506 | | |
| | 5.9543 | 8.0 | 7848 | 5.9506 | | |
| | 5.9535 | 9.0 | 8829 | 5.9506 | | |
| | 5.9519 | 10.0 | 9810 | 5.9506 | | |
| ### Framework versions | |
| - Transformers 4.33.1 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.13.3 | |