update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- harem
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-base-portuguese-cased_harem-sm-first-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: harem
+      type: harem
+      args: selective
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7455830388692579
+    - name: Recall
+      type: recall
+      value: 0.8053435114503816
+    - name: F1
+      type: f1
+      value: 0.7743119266055045
+    - name: Accuracy
+      type: accuracy
+      value: 0.964875491480996
+---
+
+<!-- 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. -->
+
+# bert-base-portuguese-cased_harem-sm-first-ner
+
+This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the harem dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1952
+- Precision: 0.7456
+- Recall: 0.8053
+- F1: 0.7743
+- Accuracy: 0.9649
+
+## 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: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1049        | 1.0   | 2517 | 0.1955          | 0.6601    | 0.7710 | 0.7113 | 0.9499   |
+| 0.0622        | 2.0   | 5034 | 0.2097          | 0.7314    | 0.7901 | 0.7596 | 0.9554   |
+| 0.0318        | 3.0   | 7551 | 0.1952          | 0.7456    | 0.8053 | 0.7743 | 0.9649   |
+
+
+### Framework versions
+
+- Transformers 4.18.0
+- Pytorch 1.10.2+cu102
+- Datasets 2.2.2
+- Tokenizers 0.12.1