luisgasco/biomedical-roberta-finetuned-iomed_task_b4_ep20

README.md

@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-es) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.0160
-- Precision: 0.1934
-- Recall: 0.3953
-- F1: 0.2597
-- Accuracy: 0.7681
+- Loss: 1.0582
+- Precision: 0.2269
+- Recall: 0.4283
+- F1: 0.2966
+- Accuracy: 0.7695
 
 ## Model description
 
@@ -43,28 +43,28 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2.2e-06
+- learning_rate: 2.1e-06
 - train_batch_size: 4
 - eval_batch_size: 4
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 13
+- num_epochs: 20
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 2.1178        | 1.3   | 988  | 1.3039          | 0.0802    | 0.2102 | 0.1161 | 0.7321   |
-| 1.2448        | 2.6   | 1976 | 1.1444          | 0.1161    | 0.2811 | 0.1643 | 0.7568   |
-| 1.0297        | 3.9   | 2964 | 1.0724          | 0.1484    | 0.3299 | 0.2047 | 0.7658   |
-| 0.9496        | 5.2   | 3952 | 1.0342          | 0.1676    | 0.3583 | 0.2284 | 0.7685   |
-| 0.88          | 6.5   | 4940 | 1.0465          | 0.1805    | 0.3795 | 0.2446 | 0.7660   |
-| 0.8168        | 7.8   | 5928 | 1.0253          | 0.1713    | 0.3638 | 0.2329 | 0.7624   |
-| 0.7863        | 9.1   | 6916 | 1.0293          | 0.1754    | 0.3622 | 0.2364 | 0.7641   |
-| 0.784         | 10.4  | 7904 | 1.0159          | 0.1920    | 0.3890 | 0.2571 | 0.7648   |
-| 0.7161        | 11.7  | 8892 | 1.0121          | 0.1853    | 0.3819 | 0.2495 | 0.7654   |
-| 0.7629        | 13.0  | 9880 | 1.0160          | 0.1934    | 0.3953 | 0.2597 | 0.7681   |
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.2536        | 2.0   | 1520  | 1.2135          | 0.1082    | 0.2685 | 0.1542 | 0.7422   |
+| 1.0249        | 4.0   | 3040  | 1.0510          | 0.1448    | 0.3244 | 0.2002 | 0.7650   |
+| 0.9           | 6.0   | 4560  | 1.0098          | 0.1587    | 0.3512 | 0.2186 | 0.7694   |
+| 0.8002        | 8.0   | 6080  | 1.0143          | 0.1835    | 0.3795 | 0.2474 | 0.7664   |
+| 0.7195        | 10.0  | 7600  | 1.0173          | 0.2007    | 0.4055 | 0.2685 | 0.7691   |
+| 0.693         | 12.0  | 9120  | 1.0218          | 0.1991    | 0.4079 | 0.2676 | 0.7683   |
+| 0.6139        | 14.0  | 10640 | 1.0394          | 0.2063    | 0.4071 | 0.2738 | 0.7672   |
+| 0.616         | 16.0  | 12160 | 1.0376          | 0.2141    | 0.4142 | 0.2823 | 0.7695   |
+| 0.5911        | 18.0  | 13680 | 1.0491          | 0.2240    | 0.4268 | 0.2938 | 0.7697   |
+| 0.6042        | 20.0  | 15200 | 1.0582          | 0.2269    | 0.4283 | 0.2966 | 0.7695   |
 
 
 ### Framework versions

pytorch_model.bin

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training_args.bin

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