Upload README.md

README.md

@@ -1,54 +1,31 @@
 ---
 library_name: transformers
-base_model: models/distill-robertalex-3L-trained
 tags:
 - generated_from_trainer
 datasets:
 - adalbertojunior/entities
-metrics:
-- precision
-- recall
-- f1
-- accuracy
 model-index:
-- name: test_v6
-  results:
-  - task:
-      name: Token Classification
-      type: token-classification
-    dataset:
-      name: adalbertojunior/entities
-      type: adalbertojunior/entities
-      config: segmentacao
-      split: validation
-      args: segmentacao
-    metrics:
-    - name: Precision
-      type: precision
-      value: 0.7678083439606486
-    - name: Recall
-      type: recall
-      value: 0.8550415905863258
-    - name: F1
-      type: f1
-      value: 0.8090804377039739
-    - name: Accuracy
-      type: accuracy
-      value: 0.9699217442249749
+- name: test_v4
+  results: []
 ---
 
 <!-- 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. -->
 
-# test_v6
+# test_v4
 
-This model is a fine-tuned version of [models/distill-robertalex-3L-trained](https://huggingface.co/models/distill-robertalex-3L-trained) on the adalbertojunior/entities dataset.
+This model is a fine-tuned version of [./models/entities/test_v4](https://huggingface.co/./models/entities/test_v4) on the adalbertojunior/entities dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1536
-- Precision: 0.7678
-- Recall: 0.8550
-- F1: 0.8091
-- Accuracy: 0.9699
+- eval_loss: 0.1648
+- eval_model_preparation_time: 0.0009
+- eval_precision: 0.8411
+- eval_recall: 0.8900
+- eval_f1: 0.8649
+- eval_accuracy: 0.9744
+- eval_runtime: 177.3832
+- eval_samples_per_second: 27.962
+- eval_steps_per_second: 27.962
+- step: 0
 
 ## Model description
 
@@ -78,39 +55,6 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_steps: 500
 - num_epochs: 20.0
 
-### Training results
-
-| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0925        | 19.3898 | 7000 | 0.1536          | 0.7678    | 0.8550 | 0.8091 | 0.9699   |
-
-### Test set results
-
-| Label               | Precision | Recall | F1-Score | Support |
-|---------------------|-----------|--------|----------|---------|
-| ATRIBUICAO         | 0.82      | 0.82   | 0.82     | 221     |
-| DECISAO            | 0.81      | 0.82   | 0.82     | 544     |
-| FUNCAO             | 0.94      | 0.89   | 0.91     | 486     |
-| FUNDAMENTO         | 0.89      | 0.83   | 0.86     | 1501    |
-| LOCAL              | 0.85      | 0.84   | 0.85     | 245     |
-| ORGANIZACAO        | 0.90      | 0.86   | 0.88     | 626     |
-| PEDIDO             | 0.86      | 0.81   | 0.83     | 4341    |
-| PESSOA             | 0.95      | 0.94   | 0.95     | 654     |
-| REFLEXO            | 0.85      | 0.84   | 0.85     | 358     |
-| TIPO_ACAO          | 0.93      | 0.89   | 0.91     | 341     |
-| TRIBUNAL           | 0.96      | 0.92   | 0.94     | 190     |
-| VALOR_ACORDO       | 0.91      | 0.71   | 0.79     | 41      |
-| VALOR_CAUSA        | 0.89      | 0.92   | 0.90     | 62      |
-| VALOR_CONDENACAO   | 0.89      | 0.76   | 0.82     | 72      |
-| VALOR_CUSTAS       | 0.95      | 0.93   | 0.94     | 134     |
-| VALOR_PEDIDO       | 0.94      | 0.81   | 0.87     | 308     |
-| VARA               | 0.95      | 0.96   | 0.96     | 81      |
-| **micro avg**      | 0.88      | 0.84   | 0.86     | 10205   |
-| **macro avg**      | 0.90      | 0.86   | 0.88     | 10205   |
-| **weighted avg**   | 0.88      | 0.84   | 0.86     | 10205   |
-
-
-
 ### Framework versions
 
 - Transformers 4.47.1