Hugging Face's logo

dile3
/
biobert-ner-model

Token Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
Community
dile3 commited on
Commit
b29b984
·
verified ·
1 Parent(s): 805802e

dile3/biobert-ner-ncbi

Browse files
Files changed (3) hide show
  1. README.md +27 -20
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -15,15 +15,15 @@ should probably proofread and complete it, then remove this comment. -->
15
 
16
  This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
- - Loss: 0.0386
19
- - Compositemention: {'precision': 0.8, 'recall': 0.9142857142857143, 'f1': 0.8533333333333333, 'number': 35}
20
- - Diseaseclass: {'precision': 0.5341614906832298, 'recall': 0.6825396825396826, 'f1': 0.5993031358885017, 'number': 126}
21
- - Modifier: {'precision': 0.7014925373134329, 'recall': 0.8785046728971962, 'f1': 0.7800829875518672, 'number': 214}
22
- - Specificdisease: {'precision': 0.8254716981132075, 'recall': 0.8495145631067961, 'f1': 0.8373205741626795, 'number': 412}
23
- - Overall Precision: 0.7346
24
- - Overall Recall: 0.8335
25
- - Overall F1: 0.7810
26
- - Overall Accuracy: 0.9934
27
 
28
  ## Model description
29
 
@@ -42,9 +42,9 @@ More information needed
42
  ### Training hyperparameters
43
 
44
  The following hyperparameters were used during training:
45
- - learning_rate: 3e-05
46
- - train_batch_size: 16
47
- - eval_batch_size: 16
48
  - seed: 42
49
  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
50
  - lr_scheduler_type: linear
@@ -52,14 +52,21 @@ The following hyperparameters were used during training:
52
 
53
  ### Training results
54
 
55
- | Training Loss | Epoch | Step | Validation Loss | Compositemention | Diseaseclass | Modifier | Specificdisease | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
56
- |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
57
- | 0.005 | 1.0 | 359 | 0.0271 | {'precision': 0.725, 'recall': 0.8285714285714286, 'f1': 0.7733333333333333, 'number': 35} | {'precision': 0.5263157894736842, 'recall': 0.7142857142857143, 'f1': 0.6060606060606061, 'number': 126} | {'precision': 0.7198443579766537, 'recall': 0.8644859813084113, 'f1': 0.7855626326963906, 'number': 214} | {'precision': 0.8103448275862069, 'recall': 0.7985436893203883, 'f1': 0.80440097799511, 'number': 412} | 0.7243 | 0.8043 | 0.7622 | 0.9939 |
58
- | 0.0049 | 2.0 | 718 | 0.0277 | {'precision': 0.775, 'recall': 0.8857142857142857, 'f1': 0.8266666666666667, 'number': 35} | {'precision': 0.5891472868217055, 'recall': 0.6031746031746031, 'f1': 0.596078431372549, 'number': 126} | {'precision': 0.7195121951219512, 'recall': 0.8271028037383178, 'f1': 0.7695652173913042, 'number': 214} | {'precision': 0.7707423580786026, 'recall': 0.8567961165048543, 'f1': 0.8114942528735631, 'number': 412} | 0.7297 | 0.8094 | 0.7675 | 0.9934 |
59
- | 0.0031 | 3.0 | 1077 | 0.0330 | {'precision': 0.7142857142857143, 'recall': 0.8571428571428571, 'f1': 0.7792207792207793, 'number': 35} | {'precision': 0.4939759036144578, 'recall': 0.6507936507936508, 'f1': 0.5616438356164383, 'number': 126} | {'precision': 0.7368421052631579, 'recall': 0.8504672897196262, 'f1': 0.789587852494577, 'number': 214} | {'precision': 0.8076923076923077, 'recall': 0.866504854368932, 'f1': 0.8360655737704917, 'number': 412} | 0.7258 | 0.8272 | 0.7732 | 0.9935 |
60
- | 0.0008 | 4.0 | 1436 | 0.0324 | {'precision': 0.7567567567567568, 'recall': 0.8, 'f1': 0.7777777777777778, 'number': 35} | {'precision': 0.6014492753623188, 'recall': 0.6587301587301587, 'f1': 0.6287878787878789, 'number': 126} | {'precision': 0.746938775510204, 'recall': 0.8551401869158879, 'f1': 0.7973856209150327, 'number': 214} | {'precision': 0.8389423076923077, 'recall': 0.8470873786407767, 'f1': 0.8429951690821257, 'number': 412} | 0.7691 | 0.8170 | 0.7924 | 0.9940 |
61
- | 0.0019 | 5.0 | 1795 | 0.0314 | {'precision': 0.7804878048780488, 'recall': 0.9142857142857143, 'f1': 0.8421052631578947, 'number': 35} | {'precision': 0.6356589147286822, 'recall': 0.6507936507936508, 'f1': 0.6431372549019608, 'number': 126} | {'precision': 0.7615062761506276, 'recall': 0.8504672897196262, 'f1': 0.8035320088300221, 'number': 214} | {'precision': 0.8148148148148148, 'recall': 0.8543689320388349, 'f1': 0.8341232227488151, 'number': 412} | 0.7705 | 0.8234 | 0.7961 | 0.9939 |
62
- | 0.0017 | 6.0 | 2154 | 0.0386 | {'precision': 0.8, 'recall': 0.9142857142857143, 'f1': 0.8533333333333333, 'number': 35} | {'precision': 0.5341614906832298, 'recall': 0.6825396825396826, 'f1': 0.5993031358885017, 'number': 126} | {'precision': 0.7014925373134329, 'recall': 0.8785046728971962, 'f1': 0.7800829875518672, 'number': 214} | {'precision': 0.8254716981132075, 'recall': 0.8495145631067961, 'f1': 0.8373205741626795, 'number': 412} | 0.7346 | 0.8335 | 0.7810 | 0.9934 |
 
 
 
 
 
 
 
63
 
64
 
65
  ### Framework versions
 
15
 
16
  This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 0.0334
19
+ - Compositemention: {'precision': 0.6, 'recall': 0.7714285714285715, 'f1': 0.675, 'number': 35}
20
+ - Diseaseclass: {'precision': 0.6206896551724138, 'recall': 0.7142857142857143, 'f1': 0.6642066420664207, 'number': 126}
21
+ - Modifier: {'precision': 0.7510204081632653, 'recall': 0.8598130841121495, 'f1': 0.8017429193899782, 'number': 214}
22
+ - Specificdisease: {'precision': 0.8186046511627907, 'recall': 0.8543689320388349, 'f1': 0.836104513064133, 'number': 412}
23
+ - Overall Precision: 0.7549
24
+ - Overall Recall: 0.8297
25
+ - Overall F1: 0.7906
26
+ - Overall Accuracy: 0.9942
27
 
28
  ## Model description
29
 
 
42
  ### Training hyperparameters
43
 
44
  The following hyperparameters were used during training:
45
+ - learning_rate: 2e-05
46
+ - train_batch_size: 32
47
+ - eval_batch_size: 32
48
  - seed: 42
49
  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
50
  - lr_scheduler_type: linear
 
52
 
53
  ### Training results
54
 
55
+ | Training Loss | Epoch | Step | Validation Loss | Compositemention | Diseaseclass | Modifier | Specificdisease | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
56
+ |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
57
+ | 0.0302 | 1.0 | 180 | 0.0324 | {'precision': 0.25, 'recall': 0.22857142857142856, 'f1': 0.23880597014925375, 'number': 35} | {'precision': 0.2804878048780488, 'recall': 0.18253968253968253, 'f1': 0.22115384615384615, 'number': 126} | {'precision': 0.5510204081632653, 'recall': 0.6308411214953271, 'f1': 0.588235294117647, 'number': 214} | {'precision': 0.5815217391304348, 'recall': 0.779126213592233, 'f1': 0.6659751037344398, 'number': 412} | 0.5346 | 0.6188 | 0.5736 | 0.9911 |
58
+ | 0.0185 | 2.0 | 360 | 0.0241 | {'precision': 0.3958333333333333, 'recall': 0.5428571428571428, 'f1': 0.4578313253012048, 'number': 35} | {'precision': 0.487012987012987, 'recall': 0.5952380952380952, 'f1': 0.5357142857142857, 'number': 126} | {'precision': 0.7046413502109705, 'recall': 0.780373831775701, 'f1': 0.7405764966740577, 'number': 214} | {'precision': 0.7154471544715447, 'recall': 0.8543689320388349, 'f1': 0.7787610619469026, 'number': 412} | 0.6584 | 0.7789 | 0.7136 | 0.9929 |
59
+ | 0.012 | 3.0 | 540 | 0.0220 | {'precision': 0.5, 'recall': 0.6285714285714286, 'f1': 0.5569620253164557, 'number': 35} | {'precision': 0.5826086956521739, 'recall': 0.5317460317460317, 'f1': 0.5560165975103734, 'number': 126} | {'precision': 0.6896551724137931, 'recall': 0.8411214953271028, 'f1': 0.7578947368421054, 'number': 214} | {'precision': 0.7658227848101266, 'recall': 0.8810679611650486, 'f1': 0.8194130925507901, 'number': 412} | 0.7069 | 0.8030 | 0.7519 | 0.9942 |
60
+ | 0.0092 | 4.0 | 720 | 0.0224 | {'precision': 0.7894736842105263, 'recall': 0.8571428571428571, 'f1': 0.8219178082191781, 'number': 35} | {'precision': 0.5506329113924051, 'recall': 0.6904761904761905, 'f1': 0.6126760563380282, 'number': 126} | {'precision': 0.6920152091254753, 'recall': 0.8504672897196262, 'f1': 0.7631027253668764, 'number': 214} | {'precision': 0.7671840354767184, 'recall': 0.8398058252427184, 'f1': 0.8018539976825029, 'number': 412} | 0.7088 | 0.8196 | 0.7602 | 0.9940 |
61
+ | 0.0067 | 5.0 | 900 | 0.0264 | {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 35} | {'precision': 0.5317919075144508, 'recall': 0.7301587301587301, 'f1': 0.6153846153846153, 'number': 126} | {'precision': 0.6977611940298507, 'recall': 0.8738317757009346, 'f1': 0.7759336099585062, 'number': 214} | {'precision': 0.7910798122065728, 'recall': 0.8179611650485437, 'f1': 0.8042959427207637, 'number': 412} | 0.7085 | 0.8183 | 0.7594 | 0.9937 |
62
+ | 0.0046 | 6.0 | 1080 | 0.0263 | {'precision': 0.75, 'recall': 0.8571428571428571, 'f1': 0.7999999999999999, 'number': 35} | {'precision': 0.5625, 'recall': 0.7142857142857143, 'f1': 0.6293706293706294, 'number': 126} | {'precision': 0.7357723577235772, 'recall': 0.8457943925233645, 'f1': 0.7869565217391304, 'number': 214} | {'precision': 0.8023529411764706, 'recall': 0.8276699029126213, 'f1': 0.8148148148148149, 'number': 412} | 0.7371 | 0.8158 | 0.7744 | 0.9943 |
63
+ | 0.0032 | 7.0 | 1260 | 0.0293 | {'precision': 0.5681818181818182, 'recall': 0.7142857142857143, 'f1': 0.6329113924050633, 'number': 35} | {'precision': 0.5370370370370371, 'recall': 0.6904761904761905, 'f1': 0.6041666666666667, 'number': 126} | {'precision': 0.7215686274509804, 'recall': 0.8598130841121495, 'f1': 0.7846481876332623, 'number': 214} | {'precision': 0.795774647887324, 'recall': 0.8228155339805825, 'f1': 0.8090692124105012, 'number': 412} | 0.7159 | 0.8069 | 0.7587 | 0.9937 |
64
+ | 0.002 | 8.0 | 1440 | 0.0294 | {'precision': 0.5416666666666666, 'recall': 0.7428571428571429, 'f1': 0.6265060240963857, 'number': 35} | {'precision': 0.5083798882681564, 'recall': 0.7222222222222222, 'f1': 0.5967213114754099, 'number': 126} | {'precision': 0.7510548523206751, 'recall': 0.8317757009345794, 'f1': 0.7893569844789358, 'number': 214} | {'precision': 0.7736720554272517, 'recall': 0.8131067961165048, 'f1': 0.7928994082840236, 'number': 412} | 0.7023 | 0.8005 | 0.7482 | 0.9939 |
65
+ | 0.0007 | 9.0 | 1620 | 0.0300 | {'precision': 0.8648648648648649, 'recall': 0.9142857142857143, 'f1': 0.888888888888889, 'number': 35} | {'precision': 0.6666666666666666, 'recall': 0.7301587301587301, 'f1': 0.696969696969697, 'number': 126} | {'precision': 0.746938775510204, 'recall': 0.8551401869158879, 'f1': 0.7973856209150327, 'number': 214} | {'precision': 0.8393285371702638, 'recall': 0.8495145631067961, 'f1': 0.8443908323281061, 'number': 412} | 0.7849 | 0.8348 | 0.8091 | 0.9946 |
66
+ | 0.0037 | 10.0 | 1800 | 0.0314 | {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 35} | {'precision': 0.6312056737588653, 'recall': 0.7063492063492064, 'f1': 0.6666666666666667, 'number': 126} | {'precision': 0.7418032786885246, 'recall': 0.8457943925233645, 'f1': 0.7903930131004366, 'number': 214} | {'precision': 0.8183962264150944, 'recall': 0.8422330097087378, 'f1': 0.8301435406698564, 'number': 412} | 0.7579 | 0.8196 | 0.7875 | 0.9944 |
67
+ | 0.0011 | 11.0 | 1980 | 0.0319 | {'precision': 0.7073170731707317, 'recall': 0.8285714285714286, 'f1': 0.7631578947368421, 'number': 35} | {'precision': 0.6148648648648649, 'recall': 0.7222222222222222, 'f1': 0.6642335766423357, 'number': 126} | {'precision': 0.7479674796747967, 'recall': 0.8598130841121495, 'f1': 0.7999999999999999, 'number': 214} | {'precision': 0.8341232227488151, 'recall': 0.8543689320388349, 'f1': 0.8441247002398082, 'number': 412} | 0.7655 | 0.8335 | 0.7981 | 0.9945 |
68
+ | 0.0004 | 12.0 | 2160 | 0.0347 | {'precision': 0.6904761904761905, 'recall': 0.8285714285714286, 'f1': 0.7532467532467533, 'number': 35} | {'precision': 0.6328125, 'recall': 0.6428571428571429, 'f1': 0.6377952755905513, 'number': 126} | {'precision': 0.7811158798283262, 'recall': 0.8504672897196262, 'f1': 0.814317673378076, 'number': 214} | {'precision': 0.8028169014084507, 'recall': 0.8300970873786407, 'f1': 0.8162291169451074, 'number': 412} | 0.7648 | 0.8056 | 0.7847 | 0.9942 |
69
+ | 0.0014 | 13.0 | 2340 | 0.0334 | {'precision': 0.6, 'recall': 0.7714285714285715, 'f1': 0.675, 'number': 35} | {'precision': 0.6206896551724138, 'recall': 0.7142857142857143, 'f1': 0.6642066420664207, 'number': 126} | {'precision': 0.7510204081632653, 'recall': 0.8598130841121495, 'f1': 0.8017429193899782, 'number': 214} | {'precision': 0.8186046511627907, 'recall': 0.8543689320388349, 'f1': 0.836104513064133, 'number': 412} | 0.7549 | 0.8297 | 0.7906 | 0.9942 |
70
 
71
 
72
  ### Framework versions
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:82d9da89bff069b6a0418e5e0df8233fe3833053f7b7308b37cd31d8a4c3aa91
3
  size 430929740
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3f1c323a5cf9f4420eb984d335d1c479b6bb693dadc937074ce0fc3e62bc181b
3
  size 430929740
training_args.bin CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:8606adc4ad54e20931bdbf03a89c6dd909f34ef6d6447a790a87c6387ec58ba3
3
  size 5240
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cc3d79d136cec7138e317dc45d1e880ea44b748dde4856a1e6f63709f6e5b5d7
3
  size 5240