Training complete - XLNet-base-LeNER

README.md

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+---
+license: mit
+base_model: xlnet/xlnet-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- lener_br
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: XLNet-base_LeNER-Br
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: lener_br
+      type: lener_br
+      config: lener_br
+      split: validation
+      args: lener_br
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8062054933875891
+    - name: Recall
+      type: recall
+      value: 0.872317006053935
+    - name: F1
+      type: f1
+      value: 0.8379592915675389
+    - name: Accuracy
+      type: accuracy
+      value: 0.9783680282796544
+---
+
+<!-- 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. -->
+
+# XLNet-base_LeNER-Br
+
+This model is a fine-tuned version of [xlnet/xlnet-base-cased](https://huggingface.co/xlnet/xlnet-base-cased) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Precision: 0.8062
+- Recall: 0.8723
+- F1: 0.8380
+- Accuracy: 0.9784
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2531        | 1.0   | 979  | nan             | 0.6037    | 0.7788 | 0.6801 | 0.9602   |
+| 0.0531        | 2.0   | 1958 | nan             | 0.6865    | 0.8184 | 0.7467 | 0.9657   |
+| 0.0344        | 3.0   | 2937 | nan             | 0.7079    | 0.8321 | 0.7650 | 0.9697   |
+| 0.0214        | 4.0   | 3916 | nan             | 0.7739    | 0.8514 | 0.8108 | 0.9765   |
+| 0.0176        | 5.0   | 4895 | nan             | 0.7407    | 0.8520 | 0.7924 | 0.9712   |
+| 0.0109        | 6.0   | 5874 | nan             | 0.7984    | 0.8696 | 0.8325 | 0.9773   |
+| 0.0093        | 7.0   | 6853 | nan             | 0.7944    | 0.8657 | 0.8285 | 0.9778   |
+| 0.0056        | 8.0   | 7832 | nan             | 0.8130    | 0.8756 | 0.8431 | 0.9779   |
+| 0.0041        | 9.0   | 8811 | nan             | 0.8171    | 0.8751 | 0.8451 | 0.9781   |
+| 0.0034        | 10.0  | 9790 | nan             | 0.8062    | 0.8723 | 0.8380 | 0.9784   |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1