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: 0.1823
-- 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}
+- Loss: 0.1810
+- Study Test: {'precision': 0.8215384615384616, 'recall': 0.8841059602649006, 'f1': 0.8516746411483254, 'number': 302}
+- Manifestation: {'precision': 0.8041958041958042, 'recall': 0.905511811023622, 'f1': 0.8518518518518519, 'number': 127}
+- Finding: {'precision': 0.6886657101865137, 'recall': 0.7570977917981072, 'f1': 0.7212622088655146, 'number': 634}
+- Specimen: {'precision': 0.7944162436548223, 'recall': 0.8236842105263158, 'f1': 0.8087855297157622, 'number': 380}
+- Dose: {'precision': 0.8647540983606558, 'recall': 0.9461883408071748, 'f1': 0.9036402569593148, 'number': 223}
+- Dose Qualification: {'precision': 0.65, 'recall': 0.8125, 'f1': 0.7222222222222223, 'number': 32}
+- Sex: {'precision': 0.9285714285714286, 'recall': 0.9285714285714286, 'f1': 0.9285714285714286, 'number': 84}
+- Group: {'precision': 0.5666666666666667, 'recall': 0.6938775510204082, 'f1': 0.6238532110091742, 'number': 49}
 
 ## Model description
 
@@ -51,16 +51,16 @@ 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  | 0.2018          | {'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}               |
+| Training Loss | Epoch | Step | Validation Loss | Study Test                                                                                               | Manifestation                                                                                            | Finding                                                                                                  | Specimen                                                                                                 | Dose                                                                                                     | Dose Qualification                                                                          | Sex                                                                                                     | Group                                                                                                   |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 257  | 0.2005          | {'precision': 0.6658227848101266, 'recall': 0.8708609271523179, 'f1': 0.7546628407460545, 'number': 302} | {'precision': 0.7647058823529411, 'recall': 0.9212598425196851, 'f1': 0.8357142857142856, 'number': 127} | {'precision': 0.6425339366515838, 'recall': 0.6719242902208202, 'f1': 0.6569005397070162, 'number': 634} | {'precision': 0.7099767981438515, 'recall': 0.8052631578947368, 'f1': 0.75462392108508, 'number': 380}   | {'precision': 0.8969957081545065, 'recall': 0.9372197309417041, 'f1': 0.9166666666666667, 'number': 223} | {'precision': 0.6764705882352942, 'recall': 0.71875, 'f1': 0.696969696969697, 'number': 32} | {'precision': 0.7448979591836735, 'recall': 0.8690476190476191, 'f1': 0.8021978021978022, 'number': 84} | {'precision': 0.3880597014925373, 'recall': 0.5306122448979592, 'f1': 0.4482758620689655, 'number': 49} |
+| 0.2932        | 2.0   | 514  | 0.1689          | {'precision': 0.8170347003154574, 'recall': 0.8576158940397351, 'f1': 0.8368336025848143, 'number': 302} | {'precision': 0.8226950354609929, 'recall': 0.9133858267716536, 'f1': 0.8656716417910448, 'number': 127} | {'precision': 0.6904400606980273, 'recall': 0.7176656151419558, 'f1': 0.7037896365042536, 'number': 634} | {'precision': 0.7746478873239436, 'recall': 0.868421052631579, 'f1': 0.8188585607940446, 'number': 380}  | {'precision': 0.8870292887029289, 'recall': 0.9506726457399103, 'f1': 0.9177489177489178, 'number': 223} | {'precision': 0.7567567567567568, 'recall': 0.875, 'f1': 0.8115942028985507, 'number': 32}  | {'precision': 0.8695652173913043, 'recall': 0.9523809523809523, 'f1': 0.909090909090909, 'number': 84}  | {'precision': 0.6, 'recall': 0.673469387755102, 'f1': 0.6346153846153846, 'number': 49}                 |
+| 0.2932        | 3.0   | 771  | 0.1810          | {'precision': 0.8215384615384616, 'recall': 0.8841059602649006, 'f1': 0.8516746411483254, 'number': 302} | {'precision': 0.8041958041958042, 'recall': 0.905511811023622, 'f1': 0.8518518518518519, 'number': 127}  | {'precision': 0.6886657101865137, 'recall': 0.7570977917981072, 'f1': 0.7212622088655146, 'number': 634} | {'precision': 0.7944162436548223, 'recall': 0.8236842105263158, 'f1': 0.8087855297157622, 'number': 380} | {'precision': 0.8647540983606558, 'recall': 0.9461883408071748, 'f1': 0.9036402569593148, 'number': 223} | {'precision': 0.65, 'recall': 0.8125, 'f1': 0.7222222222222223, 'number': 32}               | {'precision': 0.9285714285714286, 'recall': 0.9285714285714286, 'f1': 0.9285714285714286, 'number': 84} | {'precision': 0.5666666666666667, 'recall': 0.6938775510204082, 'f1': 0.6238532110091742, 'number': 49} |
 
 
 ### Framework versions
 
-- Transformers 4.35.2
-- Pytorch 2.1.0+cu118
-- Datasets 2.15.0
-- Tokenizers 0.15.0
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

config.json

@@ -57,7 +57,7 @@
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
   "torch_dtype": "float32",
-  "transformers_version": "4.35.2",
+  "transformers_version": "4.38.2",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 28996

model.safetensors

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runs/Apr04_21-52-04_ec306db945fe/events.out.tfevents.1712267525.ec306db945fe.765.0

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training_args.bin

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