End of training

README.md

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+---
+license: mit
+base_model: deepset/gbert-large
+tags:
+- generated_from_trainer
+datasets:
+- universal_dependencies
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: gbert-large-upos
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: universal_dependencies
+      type: universal_dependencies
+      config: de_gsd
+      split: validation
+      args: de_gsd
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.825291976991079
+    - name: Recall
+      type: recall
+      value: 0.7826990832215603
+    - name: F1
+      type: f1
+      value: 0.7912197452035137
+    - name: Accuracy
+      type: accuracy
+      value: 0.9413806706114398
+---
+
+<!-- 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. -->
+
+# gbert-large-upos
+
+This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the universal_dependencies dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1996
+- Precision: 0.8253
+- Recall: 0.7827
+- F1: 0.7912
+- Accuracy: 0.9414
+
+## 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  | 0.3197          | 0.8098    | 0.7291 | 0.7486 | 0.8936   |
+| No log        | 2.0   | 876  | 0.2261          | 0.8287    | 0.7679 | 0.7832 | 0.9269   |
+| No log        | 3.0   | 1314 | 0.1996          | 0.8253    | 0.7827 | 0.7912 | 0.9414   |
+| No log        | 4.0   | 1752 | 0.2183          | 0.8162    | 0.8006 | 0.8041 | 0.9435   |
+| No log        | 5.0   | 2190 | 0.2120          | 0.8198    | 0.8025 | 0.8074 | 0.9496   |
+| No log        | 6.0   | 2628 | 0.2339          | 0.8207    | 0.8068 | 0.8116 | 0.9489   |
+| No log        | 7.0   | 3066 | 0.2728          | 0.8156    | 0.8045 | 0.8071 | 0.9486   |
+| No log        | 8.0   | 3504 | 0.2790          | 0.8205    | 0.8110 | 0.8132 | 0.9527   |
+| No log        | 9.0   | 3942 | 0.2854          | 0.8306    | 0.8096 | 0.8146 | 0.9527   |
+| No log        | 10.0  | 4380 | 0.2906          | 0.8299    | 0.8115 | 0.8151 | 0.9534   |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

model.safetensors

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