End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- financial_phrasebank
+metrics:
+- accuracy
+- f1
+model-index:
+- name: finetuning-sentiment-model-3000-samples
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: financial_phrasebank
+      type: financial_phrasebank
+      config: sentences_allagree
+      split: train
+      args: sentences_allagree
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9801324503311258
+    - name: F1
+      type: f1
+      value: 0.9726332415417978
+---
+
+<!-- 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. -->
+
+# finetuning-sentiment-model-3000-samples
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the financial_phrasebank dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0731
+- Accuracy: 0.9801
+- F1: 0.9726
+
+## 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
+
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 3.0.0
+- Tokenizers 0.19.1

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