End of training

README.md

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+---
+license: mit
+base_model: nlptown/bert-base-multilingual-uncased-sentiment
+tags:
+- generated_from_trainer
+datasets:
+- amazon_reviews_multi
+metrics:
+- accuracy
+- f1
+model-index:
+- name: amazon_reviews_finetuning-sentiment-model-3000-samples
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: amazon_reviews_multi
+      type: amazon_reviews_multi
+      config: en
+      split: validation
+      args: en
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.58
+    - name: F1
+      type: f1
+      value: 0.5603711644808317
+---
+
+<!-- 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. -->
+
+# amazon_reviews_finetuning-sentiment-model-3000-samples
+
+This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the amazon_reviews_multi dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0099
+- Accuracy: 0.58
+- F1: 0.5604
+
+## 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: 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: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| No log        | 1.0   | 188  | 0.9821          | 0.59     | 0.5534 |
+| No log        | 2.0   | 376  | 1.0099          | 0.58     | 0.5604 |
+
+
+### Framework versions
+
+- Transformers 4.33.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3