End of training

README.md

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+---
+license: apache-2.0
+base_model: bert-large-cased
+tags:
+- generated_from_trainer
+datasets:
+- universal_dependencies
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-large-cased-upos
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: universal_dependencies
+      type: universal_dependencies
+      config: en_ewt
+      split: validation
+      args: en_ewt
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8688031595250056
+    - name: Recall
+      type: recall
+      value: 0.8557292884764056
+    - name: F1
+      type: f1
+      value: 0.8617720995316154
+    - name: Accuracy
+      type: accuracy
+      value: 0.8904395106479384
+---
+
+<!-- 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. -->
+
+# bert-large-cased-upos
+
+This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the universal_dependencies dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4114
+- Precision: 0.8688
+- Recall: 0.8557
+- F1: 0.8618
+- Accuracy: 0.8904
+
+## 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  | 0.5774          | 0.8348    | 0.7595 | 0.7893 | 0.8099   |
+| No log        | 2.0   | 876  | 0.4787          | 0.8114    | 0.7967 | 0.8000 | 0.8385   |
+| No log        | 3.0   | 1314 | 0.4345          | 0.8227    | 0.8302 | 0.8213 | 0.8601   |
+| No log        | 4.0   | 1752 | 0.4140          | 0.8257    | 0.8430 | 0.8304 | 0.8727   |
+| No log        | 5.0   | 2190 | 0.4211          | 0.8405    | 0.8525 | 0.8441 | 0.8787   |
+| No log        | 6.0   | 2628 | 0.4114          | 0.8688    | 0.8557 | 0.8618 | 0.8904   |
+| No log        | 7.0   | 3066 | 0.4582          | 0.8454    | 0.8572 | 0.8503 | 0.8911   |
+| No log        | 8.0   | 3504 | 0.4771          | 0.8447    | 0.8588 | 0.8508 | 0.8894   |
+| No log        | 9.0   | 3942 | 0.4799          | 0.8545    | 0.8626 | 0.8577 | 0.8918   |
+| No log        | 10.0  | 4380 | 0.4919          | 0.8539    | 0.8642 | 0.8579 | 0.8937   |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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