End of training

README.md

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+---
+license: apache-2.0
+base_model: jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE
+tags:
+- generated_from_trainer
+metrics:
+- f1
+- precision
+- recall
+- accuracy
+model-index:
+- name: bert-base-uncased-MLP-scirepeval-chemistry-LARGE-textCLS-RHEOLOGY-20230913-3
+  results: []
+---
+
+<!-- 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-base-uncased-MLP-scirepeval-chemistry-LARGE-textCLS-RHEOLOGY-20230913-3
+
+This model is a fine-tuned version of [jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE](https://huggingface.co/jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6836
+- F1: 0.7805
+- Precision: 0.7860
+- Recall: 0.7840
+- Accuracy: 0.7840
+
+## 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 | F1     | Precision | Recall | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
+| 1.1777        | 1.0   | 46   | 0.8465          | 0.6593 | 0.6346    | 0.7037 | 0.7037   |
+| 0.6923        | 2.0   | 92   | 0.7123          | 0.7491 | 0.7654    | 0.7593 | 0.7593   |
+| 0.4974        | 3.0   | 138  | 0.6906          | 0.7563 | 0.7667    | 0.7593 | 0.7593   |
+| 0.3789        | 4.0   | 184  | 0.6754          | 0.7645 | 0.7712    | 0.7716 | 0.7716   |
+| 0.3053        | 5.0   | 230  | 0.6836          | 0.7805 | 0.7860    | 0.7840 | 0.7840   |
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3