End of training

README.md

@@ -18,7 +18,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the cuad-qa dataset.
 It achieves the following results on the evaluation set:
-- Loss: nan
+- Loss: 56.3253
+- Jaccard: 0.1325
 
 ## Model description
 
@@ -38,27 +39,61 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 4
+- train_batch_size: 3
 - eval_batch_size: 4
 - seed: 42
 - gradient_accumulation_steps: 4
-- total_train_batch_size: 16
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- total_train_batch_size: 12
+- optimizer: Use adamw_torch with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
-- num_epochs: 3
+- num_epochs: 4
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss |
-|:-------------:|:------:|:----:|:---------------:|
-| 0.0           | 1.0    | 1404 | nan             |
-| 0.0           | 2.0    | 2808 | nan             |
-| 0.0           | 2.9984 | 4209 | nan             |
+| Training Loss | Epoch  | Step | Validation Loss | Jaccard |
+|:-------------:|:------:|:----:|:---------------:|:-------:|
+| 2931.2947     | 0.1075 | 100  | 125.3868        | 0.0261  |
+| 114.0476      | 0.2149 | 200  | 98.0385         | 0.0225  |
+| 92.3046       | 0.3224 | 300  | 86.1094         | 0.0279  |
+| 83.4547       | 0.4299 | 400  | 80.0709         | 0.0403  |
+| 80.4591       | 0.5373 | 500  | 75.2658         | 0.0433  |
+| 76.238        | 0.6448 | 600  | 71.9617         | 0.0445  |
+| 73.2576       | 0.7523 | 700  | 68.1718         | 0.0463  |
+| 70.5061       | 0.8598 | 800  | 64.2118         | 0.0536  |
+| 72.0594       | 0.9672 | 900  | 82.5902         | 0.0243  |
+| 65.2249       | 1.0742 | 1000 | 59.8434         | 0.0647  |
+| 63.2437       | 1.1816 | 1100 | 60.3719         | 0.0932  |
+| 67.1502       | 1.2891 | 1200 | 63.5264         | 0.1114  |
+| 65.1003       | 1.3966 | 1300 | 60.7845         | 0.1243  |
+| 64.7538       | 1.5040 | 1400 | 66.3558         | 0.1200  |
+| 66.7688       | 1.6115 | 1500 | 69.2212         | 0.1149  |
+| 76.4721       | 1.7190 | 1600 | 69.5449         | 0.1458  |
+| 82.2733       | 1.8264 | 1700 | 82.1182         | 0.0449  |
+| 78.7475       | 1.9339 | 1800 | 62.4942         | 0.1581  |
+| 69.5967       | 2.0408 | 1900 | 63.3104         | 0.1507  |
+| 67.6753       | 2.1483 | 2000 | 56.4553         | 0.2238  |
+| 64.0365       | 2.2558 | 2100 | 60.3552         | 0.1978  |
+| 62.561        | 2.3632 | 2200 | 55.5222         | 0.2238  |
+| 62.0848       | 2.4707 | 2300 | 51.5148         | 0.2239  |
+| 59.3192       | 2.5782 | 2400 | 56.1338         | 0.1939  |
+| 63.3072       | 2.6857 | 2500 | 55.3624         | 0.2385  |
+| 63.0132       | 2.7931 | 2600 | 48.8478         | 0.2614  |
+| 61.0742       | 2.9006 | 2700 | 57.2687         | 0.2574  |
+| 63.7064       | 3.0075 | 2800 | 58.7552         | 0.2569  |
+| 61.3371       | 3.1150 | 2900 | 62.7214         | 0.2473  |
+| 66.2795       | 3.2225 | 3000 | 60.0179         | 0.2640  |
+| 65.9729       | 3.3299 | 3100 | 59.7260         | 0.2879  |
+| 67.5846       | 3.4374 | 3200 | 63.1864         | 0.2627  |
+| 65.6924       | 3.5449 | 3300 | 58.8332         | 0.2743  |
+| 64.2456       | 3.6523 | 3400 | 59.7355         | 0.1667  |
+| 64.9793       | 3.7598 | 3500 | 57.0126         | 0.1622  |
+| 63.8452       | 3.8673 | 3600 | 56.8423         | 0.1332  |
+| 65.2058       | 3.9747 | 3700 | 56.3253         | 0.1325  |
 
 
 ### Framework versions
 
-- Transformers 4.48.0
-- Pytorch 2.2.0
+- Transformers 4.48.2
+- Pytorch 2.2.1+cu121
 - Datasets 3.2.0
 - Tokenizers 0.21.0