Hugging Face's logo

nbroad
/
deberta-v3-base-company-names

Token Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
1
nbroad HF staff commited on
Commit
6fd5484
•
1 Parent(s): dda49f8

Model save

Browse files
Files changed (13) hide show
  1. README.md +69 -0
  2. added_tokens.json +3 -0
  3. all_results.json +17 -0
  4. config.json +46 -0
  5. eval_results.json +12 -0
  6. pytorch_model.bin +3 -0
  7. special_tokens_map.json +15 -0
  8. spm.model +3 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +58 -0
  11. train_results.json +8 -0
  12. trainer_state.json +3886 -0
  13. training_args.bin +3 -0
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+ ---
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+ license: mit
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+ base_model: microsoft/deberta-v3-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: deberta-v3-base-company-names
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+ results: []
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+ ---
15
+
16
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
17
+ should probably proofread and complete it, then remove this comment. -->
18
+
19
+ # deberta-v3-base-company-names
20
+
21
+ This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0693
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+ - Precision: 0.7740
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+ - Recall: 0.7963
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+ - F1: 0.7850
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+ - Accuracy: 0.9769
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+
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+ ## Model description
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+
31
+ More information needed
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+
33
+ ## Intended uses & limitations
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+
35
+ More information needed
36
+
37
+ ## Training and evaluation data
38
+
39
+ More information needed
40
+
41
+ ## Training procedure
42
+
43
+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 8e-05
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+ - train_batch_size: 48
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 3.0
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+
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+ ### Training results
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+
57
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0752 | 1.0 | 2126 | 0.0664 | 0.7416 | 0.7979 | 0.7687 | 0.9757 |
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+ | 0.0484 | 2.0 | 4252 | 0.0652 | 0.7725 | 0.7903 | 0.7813 | 0.9768 |
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+ | 0.0415 | 3.0 | 6378 | 0.0693 | 0.7740 | 0.7963 | 0.7850 | 0.9769 |
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+
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+
64
+ ### Framework versions
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+
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+ - Transformers 4.34.1
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.16.1
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+ - Tokenizers 0.14.1
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+ {
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+ }
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