Instructions to use kSaluja/test-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kSaluja/test-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kSaluja/test-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kSaluja/test-ner") model = AutoModelForTokenClassification.from_pretrained("kSaluja/test-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from kSaluja/test-ner: direct link, hf CLI and curl.
- Browser
- Download file 1.66 kB
-
https://huggingface.co/kSaluja/test-ner/resolve/main/README.md
- Command line
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hf download hf://kSaluja/test-ner/README.md
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curl -L -o README.md https://huggingface.co/kSaluja/test-ner/resolve/main/README.md
1.66 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: test-ner | |
| 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. --> | |
| # test-ner | |
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1014 | |
| - Precision: 0.9609 | |
| - Recall: 0.9574 | |
| - F1: 0.9591 | |
| - Accuracy: 0.9732 | |
| ## 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: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | No log | 1.0 | 151 | 0.1848 | 0.9060 | 0.9184 | 0.9122 | 0.9490 | | |
| | No log | 2.0 | 302 | 0.1137 | 0.9548 | 0.9529 | 0.9538 | 0.9705 | | |
| | No log | 3.0 | 453 | 0.1014 | 0.9609 | 0.9574 | 0.9591 | 0.9732 | | |
| ### Framework versions | |
| - Transformers 4.18.0 | |
| - Pytorch 1.11.0+cu113 | |
| - Datasets 2.1.0 | |
| - Tokenizers 0.12.1 | |