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
-
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
metadata
license: apache-2.0
base_model: distilbert-base-uncased-distilled-squad
tags:
- generated_from_trainer
model-index:
- name: bankstatementmodelver2
results: []
bankstatementmodelver2
This model is a fine-tuned version of 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