End of training

README.md

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+---
+license: mit
+base_model: DTAI-KULeuven/robbert-2023-dutch-large
+tags:
+- generated_from_trainer
+datasets:
+- universal_dependencies
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: robbert-2023-dutch-large-upos
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: universal_dependencies
+      type: universal_dependencies
+      config: nl_alpino
+      split: validation
+      args: nl_alpino
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8288342749653388
+    - name: Recall
+      type: recall
+      value: 0.7844121660589751
+    - name: F1
+      type: f1
+      value: 0.7968496038696615
+    - name: Accuracy
+      type: accuracy
+      value: 0.8897894458638006
+---
+
+<!-- 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. -->
+
+# robbert-2023-dutch-large-upos
+
+This model is a fine-tuned version of [DTAI-KULeuven/robbert-2023-dutch-large](https://huggingface.co/DTAI-KULeuven/robbert-2023-dutch-large) on the universal_dependencies dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3606
+- Precision: 0.8288
+- Recall: 0.7844
+- F1: 0.7968
+- Accuracy: 0.8898
+
+## 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: 5e-05
+- train_batch_size: 16
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 438  | 0.6318          | 0.7041    | 0.6544 | 0.6603 | 0.7663   |
+| No log        | 2.0   | 876  | 0.5374          | 0.7741    | 0.6827 | 0.7090 | 0.8075   |
+| No log        | 3.0   | 1314 | 0.4318          | 0.8544    | 0.7431 | 0.7527 | 0.8595   |
+| No log        | 4.0   | 1752 | 0.4009          | 0.8254    | 0.7677 | 0.7796 | 0.8771   |
+| No log        | 5.0   | 2190 | 0.3606          | 0.8288    | 0.7844 | 0.7968 | 0.8898   |
+| No log        | 6.0   | 2628 | 0.3700          | 0.8318    | 0.8002 | 0.8108 | 0.9037   |
+| No log        | 7.0   | 3066 | 0.3733          | 0.8522    | 0.8024 | 0.8163 | 0.9071   |
+| No log        | 8.0   | 3504 | 0.3711          | 0.8659    | 0.8203 | 0.8333 | 0.9189   |
+| No log        | 9.0   | 3942 | 0.3846          | 0.8599    | 0.8222 | 0.8343 | 0.9235   |
+| No log        | 10.0  | 4380 | 0.3920          | 0.8657    | 0.8263 | 0.8397 | 0.9284   |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

model.safetensors

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