Sidecar NER: fax + cc_last4 + contact_block

README.md

@@ -1,26 +1,72 @@
 ---
+library_name: transformers
+license: apache-2.0
+base_model: bert-base-uncased
 tags:
-- ml-intern
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: privacy-filter-sidecar-bert
+  results: []
 ---
 
-# narcolepticchicken/privacy-filter-sidecar-bert
+<!-- 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. -->
 
-<!-- ml-intern-provenance -->
-## Generated by ML Intern
+# privacy-filter-sidecar-bert
 
-This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0021
+- Precision: 0.9795
+- Recall: 0.9832
+- F1: 0.9814
+- Accuracy: 0.9997
 
-- Try ML Intern: https://smolagents-ml-intern.hf.space
-- Source code: https://github.com/huggingface/ml-intern
+## Model description
 
-## Usage
+More information needed
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+## Intended uses & limitations
 
-model_id = 'narcolepticchicken/privacy-filter-sidecar-bert'
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
-```
+More information needed
 
-For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 0.1
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0028        | 1.0   | 313  | 0.0021          | 0.9790    | 0.9832 | 0.9811 | 0.9996   |
+| 0.0014        | 2.0   | 626  | 0.0020          | 0.9816    | 0.9837 | 0.9826 | 0.9997   |
+| 0.0003        | 3.0   | 939  | 0.0020          | 0.9775    | 0.9821 | 0.9798 | 0.9996   |
+| 0.0003        | 4.0   | 1252 | 0.0021          | 0.9795    | 0.9832 | 0.9814 | 0.9997   |
+
+
+### Framework versions
+
+- Transformers 5.8.0
+- Pytorch 2.11.0+cu130
+- Datasets 4.8.5
+- Tokenizers 0.22.2

model.safetensors

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