update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- dutch_social
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: robbert-twitter-sentiment-tokenized
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: dutch_social
+      type: dutch_social
+      args: dutch_social
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.814
+    - name: F1
+      type: f1
+      value: 0.8132800039281481
+    - name: Precision
+      type: precision
+      value: 0.8131073640029836
+    - name: Recall
+      type: recall
+      value: 0.814
+---
+
+<!-- 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-twitter-sentiment-tokenized
+
+This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5473
+- Accuracy: 0.814
+- F1: 0.8133
+- Precision: 0.8131
+- Recall: 0.814
+
+## 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: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.6895        | 1.0   | 282  | 0.6307          | 0.7433   | 0.7442 | 0.7500    | 0.7433 |
+| 0.4948        | 2.0   | 564  | 0.5189          | 0.8053   | 0.8062 | 0.8081    | 0.8053 |
+| 0.2642        | 3.0   | 846  | 0.5473          | 0.814    | 0.8133 | 0.8131    | 0.814  |
+
+
+### Framework versions
+
+- Transformers 4.17.0
+- Pytorch 1.11.0+cpu
+- Datasets 2.0.0
+- Tokenizers 0.11.6