Model save

README.md

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+---
+license: mit
+base_model: neuralmind/bert-base-portuguese-cased
+tags:
+- generated_from_trainer
+datasets:
+- __main__
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: absa_model_v1
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: __main__
+      type: __main__
+      config: local
+      split: test
+      args: local
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.4978690430065866
+    - name: Recall
+      type: recall
+      value: 0.5325321176958143
+    - name: F1
+      type: f1
+      value: 0.514617541049259
+    - name: Accuracy
+      type: accuracy
+      value: 0.7477374784110535
+---
+
+<!-- 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. -->
+
+# absa_model_v1
+
+This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the __main__ dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7541
+- Precision: 0.4979
+- Recall: 0.5325
+- F1: 0.5146
+- Accuracy: 0.7477
+
+## 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: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.7317        | 1.0   | 5905 | 0.7541          | 0.4979    | 0.5325 | 0.5146 | 0.7477   |
+
+
+### Framework versions
+
+- Transformers 4.36.0
+- Pytorch 2.0.1+cu117
+- Datasets 2.14.4
+- Tokenizers 0.15.0