javicorvi/pretoxtm-ner

README.md

@@ -14,15 +14,15 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1892
-- Study Test: {'precision': 0.7680288461538461, 'recall': 0.8887343532684284, 'f1': 0.8239845261121858, 'number': 719}
-- Manifestation: {'precision': 0.8245125348189415, 'recall': 0.8996960486322189, 'f1': 0.8604651162790697, 'number': 329}
-- Finding: {'precision': 0.7699175824175825, 'recall': 0.7844646606018194, 'f1': 0.7771230502599653, 'number': 1429}
-- Specimen: {'precision': 0.8063660477453581, 'recall': 0.8386206896551724, 'f1': 0.8221771467207573, 'number': 725}
-- Dose: {'precision': 0.8887043189368771, 'recall': 0.9385964912280702, 'f1': 0.9129692832764505, 'number': 570}
-- Dose Qualification: {'precision': 0.7419354838709677, 'recall': 0.8070175438596491, 'f1': 0.7731092436974789, 'number': 57}
-- Sex: {'precision': 0.9405940594059405, 'recall': 0.9405940594059405, 'f1': 0.9405940594059405, 'number': 202}
-- Group: {'precision': 0.647887323943662, 'recall': 0.8214285714285714, 'f1': 0.7244094488188976, 'number': 112}
+- Loss: 0.1823
+- Study Test: {'precision': 0.7779111644657863, 'recall': 0.9012517385257302, 'f1': 0.8350515463917525, 'number': 719}
+- Manifestation: {'precision': 0.8337950138504155, 'recall': 0.9148936170212766, 'f1': 0.872463768115942, 'number': 329}
+- Finding: {'precision': 0.7869535045107564, 'recall': 0.7935619314205739, 'f1': 0.7902439024390244, 'number': 1429}
+- Specimen: {'precision': 0.7981530343007915, 'recall': 0.8344827586206897, 'f1': 0.8159136884693189, 'number': 725}
+- Dose: {'precision': 0.8948247078464107, 'recall': 0.9403508771929825, 'f1': 0.9170230966638152, 'number': 570}
+- Dose Qualification: {'precision': 0.696969696969697, 'recall': 0.8070175438596491, 'f1': 0.7479674796747967, 'number': 57}
+- Sex: {'precision': 0.945, 'recall': 0.9356435643564357, 'f1': 0.9402985074626865, 'number': 202}
+- Group: {'precision': 0.6666666666666666, 'recall': 0.8571428571428571, 'f1': 0.75, 'number': 112}
 
 ## Model description
 
@@ -51,11 +51,11 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Study Test                                                                                               | Manifestation                                                                                            | Finding                                                                                                   | Specimen                                                                                                 | Dose                                                                                                     | Dose Qualification                                                                                      | Sex                                                                                                      | Group                                                                                                    |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 257  | 0.2128          | {'precision': 0.7071759259259259, 'recall': 0.8497913769123783, 'f1': 0.7719519898926089, 'number': 719} | {'precision': 0.7883597883597884, 'recall': 0.9057750759878419, 'f1': 0.842998585572843, 'number': 329}  | {'precision': 0.7293080054274084, 'recall': 0.7522743177046886, 'f1': 0.74061315880124, 'number': 1429}   | {'precision': 0.7443609022556391, 'recall': 0.8193103448275862, 'f1': 0.7800393959290873, 'number': 725} | {'precision': 0.7741433021806854, 'recall': 0.8719298245614036, 'f1': 0.8201320132013201, 'number': 570} | {'precision': 0.7272727272727273, 'recall': 0.5614035087719298, 'f1': 0.6336633663366337, 'number': 57} | {'precision': 0.9215686274509803, 'recall': 0.9306930693069307, 'f1': 0.9261083743842364, 'number': 202} | {'precision': 0.5673758865248227, 'recall': 0.7142857142857143, 'f1': 0.6324110671936759, 'number': 112} |
-| 0.2683        | 2.0   | 514  | 0.1918          | {'precision': 0.7720144752714113, 'recall': 0.8901251738525731, 'f1': 0.82687338501292, 'number': 719}   | {'precision': 0.803763440860215, 'recall': 0.9088145896656535, 'f1': 0.8530670470756062, 'number': 329}  | {'precision': 0.7732474964234621, 'recall': 0.7564730580825753, 'f1': 0.7647683056243367, 'number': 1429} | {'precision': 0.7907894736842105, 'recall': 0.8289655172413793, 'f1': 0.8094276094276094, 'number': 725} | {'precision': 0.8778877887788779, 'recall': 0.9333333333333333, 'f1': 0.9047619047619047, 'number': 570} | {'precision': 0.746031746031746, 'recall': 0.8245614035087719, 'f1': 0.7833333333333334, 'number': 57}  | {'precision': 0.945, 'recall': 0.9356435643564357, 'f1': 0.9402985074626865, 'number': 202}              | {'precision': 0.6298701298701299, 'recall': 0.8660714285714286, 'f1': 0.7293233082706767, 'number': 112} |
-| 0.2683        | 3.0   | 771  | 0.1892          | {'precision': 0.7680288461538461, 'recall': 0.8887343532684284, 'f1': 0.8239845261121858, 'number': 719} | {'precision': 0.8245125348189415, 'recall': 0.8996960486322189, 'f1': 0.8604651162790697, 'number': 329} | {'precision': 0.7699175824175825, 'recall': 0.7844646606018194, 'f1': 0.7771230502599653, 'number': 1429} | {'precision': 0.8063660477453581, 'recall': 0.8386206896551724, 'f1': 0.8221771467207573, 'number': 725} | {'precision': 0.8887043189368771, 'recall': 0.9385964912280702, 'f1': 0.9129692832764505, 'number': 570} | {'precision': 0.7419354838709677, 'recall': 0.8070175438596491, 'f1': 0.7731092436974789, 'number': 57} | {'precision': 0.9405940594059405, 'recall': 0.9405940594059405, 'f1': 0.9405940594059405, 'number': 202} | {'precision': 0.647887323943662, 'recall': 0.8214285714285714, 'f1': 0.7244094488188976, 'number': 112}  |
+| Training Loss | Epoch | Step | Validation Loss | Study Test                                                                                               | Manifestation                                                                                            | Finding                                                                                                   | Specimen                                                                                                 | Dose                                                                                                     | Dose Qualification                                                                                       | Sex                                                                                                      | Group                                                                                                    |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 257  | 0.2018          | {'precision': 0.7382075471698113, 'recall': 0.8706536856745479, 'f1': 0.7989789406509252, 'number': 719} | {'precision': 0.8271954674220963, 'recall': 0.8875379939209727, 'f1': 0.8563049853372434, 'number': 329} | {'precision': 0.7308461025982678, 'recall': 0.7676696990902729, 'f1': 0.7488054607508532, 'number': 1429} | {'precision': 0.7324290998766955, 'recall': 0.8193103448275862, 'f1': 0.7734375, 'number': 725}          | {'precision': 0.8511705685618729, 'recall': 0.8929824561403509, 'f1': 0.8715753424657534, 'number': 570} | {'precision': 0.5555555555555556, 'recall': 0.43859649122807015, 'f1': 0.4901960784313725, 'number': 57} | {'precision': 0.9285714285714286, 'recall': 0.900990099009901, 'f1': 0.914572864321608, 'number': 202}   | {'precision': 0.5214723926380368, 'recall': 0.7589285714285714, 'f1': 0.6181818181818183, 'number': 112} |
+| 0.2702        | 2.0   | 514  | 0.1916          | {'precision': 0.7688622754491018, 'recall': 0.8929068150208623, 'f1': 0.8262548262548264, 'number': 719} | {'precision': 0.7885117493472585, 'recall': 0.9179331306990881, 'f1': 0.848314606741573, 'number': 329}  | {'precision': 0.7945707997065297, 'recall': 0.7578726382085375, 'f1': 0.7757879656160458, 'number': 1429} | {'precision': 0.7962466487935657, 'recall': 0.8193103448275862, 'f1': 0.8076138681169274, 'number': 725} | {'precision': 0.867430441898527, 'recall': 0.9298245614035088, 'f1': 0.8975444538526672, 'number': 570}  | {'precision': 0.71875, 'recall': 0.8070175438596491, 'f1': 0.7603305785123967, 'number': 57}             | {'precision': 0.9633507853403142, 'recall': 0.9108910891089109, 'f1': 0.9363867684478371, 'number': 202} | {'precision': 0.6217948717948718, 'recall': 0.8660714285714286, 'f1': 0.7238805970149254, 'number': 112} |
+| 0.2702        | 3.0   | 771  | 0.1823          | {'precision': 0.7779111644657863, 'recall': 0.9012517385257302, 'f1': 0.8350515463917525, 'number': 719} | {'precision': 0.8337950138504155, 'recall': 0.9148936170212766, 'f1': 0.872463768115942, 'number': 329}  | {'precision': 0.7869535045107564, 'recall': 0.7935619314205739, 'f1': 0.7902439024390244, 'number': 1429} | {'precision': 0.7981530343007915, 'recall': 0.8344827586206897, 'f1': 0.8159136884693189, 'number': 725} | {'precision': 0.8948247078464107, 'recall': 0.9403508771929825, 'f1': 0.9170230966638152, 'number': 570} | {'precision': 0.696969696969697, 'recall': 0.8070175438596491, 'f1': 0.7479674796747967, 'number': 57}   | {'precision': 0.945, 'recall': 0.9356435643564357, 'f1': 0.9402985074626865, 'number': 202}              | {'precision': 0.6666666666666666, 'recall': 0.8571428571428571, 'f1': 0.75, 'number': 112}               |
 
 
 ### Framework versions

model.safetensors

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 size 430954348

runs/Dec14_12-56-42_a2e3dc378379/events.out.tfevents.1702558602.a2e3dc378379.314.1

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training_args.bin

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