trainer: training complete at 2024-02-17 18:59:08.293102.

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9372962126912465
+      value: 0.9297359274785911
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -33,11 +33,12 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the fancy_dataset dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.1948
-- : {'precision': 0.9405594405594405, 'recall': 0.9672104754749383, 'f1-score': 0.9536988041062546, 'support': 18634.0}
+- B: {'precision': 0.773955773955774, 'recall': 0.8923512747875354, 'f1-score': 0.8289473684210527, 'support': 1059.0}
+- I: {'precision': 0.9401371161027814, 'recall': 0.9597155049786629, 'f1-score': 0.949825430791756, 'support': 17575.0}
 - O: {'precision': 0.9301474791357037, 'recall': 0.8771967654986523, 'f1-score': 0.9028964598823661, 'support': 9275.0}
-- Accuracy: 0.9373
-- Macro avg: {'precision': 0.9353534598475721, 'recall': 0.9222036204867954, 'f1-score': 0.9282976319943104, 'support': 27909.0}
-- Weighted avg: {'precision': 0.9370992326621616, 'recall': 0.9372962126912465, 'f1-score': 0.9368156573551505, 'support': 27909.0}
+- Accuracy: 0.9297
+- Macro avg: {'precision': 0.8814134563980863, 'recall': 0.9097545150882835, 'f1-score': 0.893889753031725, 'support': 27909.0}
+- Weighted avg: {'precision': 0.9305115500057043, 'recall': 0.9297359274785911, 'f1-score': 0.929642834739043, 'support': 27909.0}
 
 ## Model description
 
@@ -66,16 +67,16 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss |                                                                                                                     | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
-|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.2901          | {'precision': 0.9357719203873051, 'recall': 0.9335623054631319, 'f1-score': 0.9346658070062326, 'support': 18634.0} | {'precision': 0.8671531280180277, 'recall': 0.871266846361186, 'f1-score': 0.8692051199311606, 'support': 9275.0}  | 0.9129   | {'precision': 0.9014625242026664, 'recall': 0.9024145759121589, 'f1-score': 0.9019354634686966, 'support': 27909.0} | {'precision': 0.9129678321281397, 'recall': 0.9128596510086352, 'f1-score': 0.9129112521091997, 'support': 27909.0} |
-| No log        | 2.0   | 82   | 0.2109          | {'precision': 0.9311551324929251, 'recall': 0.9711817108511324, 'f1-score': 0.9507473272216239, 'support': 18634.0} | {'precision': 0.9366296908189757, 'recall': 0.8557412398921833, 'f1-score': 0.8943602456476422, 'support': 9275.0} | 0.9328   | {'precision': 0.9338924116559504, 'recall': 0.9134614753716579, 'f1-score': 0.922553786434633, 'support': 27909.0}  | {'precision': 0.9329744928596212, 'recall': 0.9328173707406213, 'f1-score': 0.9320082043007497, 'support': 27909.0} |
-| No log        | 3.0   | 123  | 0.1948          | {'precision': 0.9405594405594405, 'recall': 0.9672104754749383, 'f1-score': 0.9536988041062546, 'support': 18634.0} | {'precision': 0.9301474791357037, 'recall': 0.8771967654986523, 'f1-score': 0.9028964598823661, 'support': 9275.0} | 0.9373   | {'precision': 0.9353534598475721, 'recall': 0.9222036204867954, 'f1-score': 0.9282976319943104, 'support': 27909.0} | {'precision': 0.9370992326621616, 'recall': 0.9372962126912465, 'f1-score': 0.9368156573551505, 'support': 27909.0} |
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2901          | {'precision': 0.8336673346693386, 'recall': 0.392823418319169, 'f1-score': 0.5340179717586649, 'support': 1059.0}  | {'precision': 0.9134376209164778, 'recall': 0.9402560455192034, 'f1-score': 0.9266528346324231, 'support': 17575.0} | {'precision': 0.8671531280180277, 'recall': 0.871266846361186, 'f1-score': 0.8692051199311606, 'support': 9275.0}  | 0.8966   | {'precision': 0.8714193612012814, 'recall': 0.7347821033998527, 'f1-score': 0.7766253087740829, 'support': 27909.0} | {'precision': 0.8950290285352085, 'recall': 0.8965566663083593, 'f1-score': 0.892662800104582, 'support': 27909.0}  |
+| No log        | 2.0   | 82   | 0.2109          | {'precision': 0.7553191489361702, 'recall': 0.8715769593956563, 'f1-score': 0.8092941692240245, 'support': 1059.0} | {'precision': 0.9298303409652446, 'recall': 0.9635846372688478, 'f1-score': 0.9464066167430425, 'support': 17575.0} | {'precision': 0.9366296908189757, 'recall': 0.8557412398921833, 'f1-score': 0.8943602456476422, 'support': 9275.0} | 0.9243   | {'precision': 0.8739263935734636, 'recall': 0.8969676121855624, 'f1-score': 0.883353677204903, 'support': 27909.0}  | {'precision': 0.9254681860164671, 'recall': 0.9242538249310258, 'f1-score': 0.9239073450445768, 'support': 27909.0} |
+| No log        | 3.0   | 123  | 0.1948          | {'precision': 0.773955773955774, 'recall': 0.8923512747875354, 'f1-score': 0.8289473684210527, 'support': 1059.0}  | {'precision': 0.9401371161027814, 'recall': 0.9597155049786629, 'f1-score': 0.949825430791756, 'support': 17575.0}  | {'precision': 0.9301474791357037, 'recall': 0.8771967654986523, 'f1-score': 0.9028964598823661, 'support': 9275.0} | 0.9297   | {'precision': 0.8814134563980863, 'recall': 0.9097545150882835, 'f1-score': 0.893889753031725, 'support': 27909.0}  | {'precision': 0.9305115500057043, 'recall': 0.9297359274785911, 'f1-score': 0.929642834739043, 'support': 27909.0}  |
 
 
 ### Framework versions
 
-- Transformers 4.37.1
-- Pytorch 2.1.2+cu121
-- Datasets 2.16.1
-- Tokenizers 0.15.1
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2