update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- sucx3_ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: histbert-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: sucx3_ner
+      type: sucx3_ner
+      config: simple_cased
+      split: validation
+      args: simple_cased
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8784308810627898
+    - name: Recall
+      type: recall
+      value: 0.9261363636363636
+    - name: F1
+      type: f1
+      value: 0.9016530520357625
+    - name: Accuracy
+      type: accuracy
+      value: 0.992218705252845
+---
+
+<!-- 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. -->
+
+# histbert-finetuned-ner
+
+This model is a fine-tuned version of [Riksarkivet/bert-base-cased-swe-historical](https://huggingface.co/Riksarkivet/bert-base-cased-swe-historical) on the sucx3_ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0495
+- Precision: 0.8784
+- Recall: 0.9261
+- F1: 0.9017
+- Accuracy: 0.9922
+
+## 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: 7
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0403        | 1.0   | 5391  | 0.0316          | 0.8496    | 0.8866 | 0.8677 | 0.9903   |
+| 0.0199        | 2.0   | 10782 | 0.0308          | 0.8814    | 0.9034 | 0.8923 | 0.9915   |
+| 0.0173        | 3.0   | 16173 | 0.0372          | 0.8698    | 0.9197 | 0.8940 | 0.9913   |
+| 0.0066        | 4.0   | 21564 | 0.0397          | 0.8783    | 0.9239 | 0.9005 | 0.9921   |
+| 0.0029        | 5.0   | 26955 | 0.0454          | 0.8855    | 0.9181 | 0.9015 | 0.9923   |
+| 0.0035        | 6.0   | 32346 | 0.0454          | 0.8834    | 0.9211 | 0.9019 | 0.9922   |
+| 0.0009        | 7.0   | 37737 | 0.0495          | 0.8784    | 0.9261 | 0.9017 | 0.9922   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3