Model save

README.md

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+---
+base_model: castorini/afriteva_v2_base
+library_name: peft
+license: apache-2.0
+metrics:
+- accuracy
+tags:
+- generated_from_trainer
+model-index:
+- name: mono_self_amh
+  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. -->
+
+# mono_self_amh
+
+This model is a fine-tuned version of [castorini/afriteva_v2_base](https://huggingface.co/castorini/afriteva_v2_base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0401
+- Accuracy: {'accuracy': 0.21588385158087275}
+
+## 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: 0.0003
+- train_batch_size: 64
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 30
+
+### Training results
+
+| Training Loss | Epoch   | Step  | Validation Loss | Accuracy                          |
+|:-------------:|:-------:|:-----:|:---------------:|:---------------------------------:|
+| 3.3329        | 1.1655  | 500   | 1.7980          | {'accuracy': 0.1844460412856023}  |
+| 1.831         | 2.3310  | 1000  | 1.5109          | {'accuracy': 0.19700483407368696} |
+| 1.5538        | 3.4965  | 1500  | 1.3840          | {'accuracy': 0.20110399790958974} |
+| 1.409         | 4.6620  | 2000  | 1.2974          | {'accuracy': 0.20477854716488111} |
+| 1.3144        | 5.8275  | 2500  | 1.2348          | {'accuracy': 0.2073588973085968}  |
+| 1.2412        | 6.9930  | 3000  | 1.1953          | {'accuracy': 0.2086000783903841}  |
+| 1.1535        | 8.1585  | 3500  | 1.1696          | {'accuracy': 0.2093349882414424}  |
+| 1.1184        | 9.3240  | 4000  | 1.1589          | {'accuracy': 0.21016788607264175} |
+| 1.0845        | 10.4895 | 4500  | 1.1263          | {'accuracy': 0.21158871178468774} |
+| 1.0656        | 11.6550 | 5000  | 1.1136          | {'accuracy': 0.21162137444473478} |
+| 1.0409        | 12.8205 | 5500  | 1.1089          | {'accuracy': 0.2121276456754638}  |
+| 1.0193        | 13.9860 | 6000  | 1.1017          | {'accuracy': 0.21263391690619285} |
+| 0.9882        | 15.1515 | 6500  | 1.0911          | {'accuracy': 0.2130748628168278}  |
+| 0.9713        | 16.3170 | 7000  | 1.0774          | {'accuracy': 0.21389142931800365} |
+| 0.9578        | 17.4825 | 7500  | 1.0679          | {'accuracy': 0.213744447347792}   |
+| 0.9462        | 18.6480 | 8000  | 1.0619          | {'accuracy': 0.21447935719885028} |
+| 0.937         | 19.8135 | 8500  | 1.0629          | {'accuracy': 0.21477332113927358} |
+| 0.9259        | 20.9790 | 9000  | 1.0580          | {'accuracy': 0.21457734517899138} |
+| 0.9075        | 22.1445 | 9500  | 1.0550          | {'accuracy': 0.21505095374967337} |
+| 0.9032        | 23.3100 | 10000 | 1.0549          | {'accuracy': 0.21513261039979095} |
+| 0.894         | 24.4755 | 10500 | 1.0494          | {'accuracy': 0.21568787562059055} |
+| 0.8849        | 25.6410 | 11000 | 1.0435          | {'accuracy': 0.21554089365037887} |
+| 0.8825        | 26.8065 | 11500 | 1.0481          | {'accuracy': 0.21575320094068462} |
+| 0.8758        | 27.9720 | 12000 | 1.0400          | {'accuracy': 0.21568787562059055} |
+| 0.8672        | 29.1375 | 12500 | 1.0401          | {'accuracy': 0.21588385158087275} |
+
+
+### Framework versions
+
+- PEFT 0.7.1
+- Transformers 4.43.3
+- Pytorch 2.4.0+cu121
+- Datasets 2.15.0
+- Tokenizers 0.19.1