Training complete

README.md

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+---
+license: mit
+base_model: roberta-base
+tags:
+- generated_from_trainer
+datasets:
+- emotion
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: RoBERTa-base-finetuned-emotion
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: emotion
+      type: emotion
+      config: split
+      split: test
+      args: split
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.933
+    - name: Precision
+      type: precision
+      value: 0.8945201216002613
+    - name: Recall
+      type: recall
+      value: 0.9001524297208578
+    - name: F1
+      type: f1
+      value: 0.8967563712384394
+---
+
+<!-- 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. -->
+
+# RoBERTa-base-finetuned-emotion
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the emotion dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1629
+- Accuracy: 0.933
+- Precision: 0.8945
+- Recall: 0.9002
+- F1: 0.8968
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.5693        | 1.0   | 500  | 0.2305          | 0.9215   | 0.8814    | 0.8854 | 0.8818 |
+| 0.1946        | 2.0   | 1000 | 0.1923          | 0.9235   | 0.8698    | 0.9268 | 0.8899 |
+| 0.1297        | 3.0   | 1500 | 0.1514          | 0.933    | 0.9060    | 0.8879 | 0.8913 |
+| 0.1041        | 4.0   | 2000 | 0.1545          | 0.9265   | 0.9165    | 0.8567 | 0.8789 |
+| 0.0826        | 5.0   | 2500 | 0.1629          | 0.933    | 0.8945    | 0.9002 | 0.8968 |
+
+
+### Framework versions
+
+- Transformers 4.33.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3

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