Rodrigo1771
commited on
Commit
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Parent(s):
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End of training
Browse files- README.md +14 -13
- all_results.json +19 -19
- eval_results.json +8 -8
- predict_results.json +8 -8
- tb/events.out.tfevents.1725049039.6b97e535edda.13440.1 +3 -0
- train.log +50 -0
- train_results.json +3 -3
- trainer_state.json +84 -84
README.md
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license: apache-2.0
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base_model: PlanTL-GOB-ES/bsc-bio-ehr-es
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tags:
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- generated_from_trainer
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datasets:
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- combined-train-drugtemist-dev-ner
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metrics:
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- precision
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- recall
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name: Token Classification
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type: token-classification
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dataset:
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name: combined-train-drugtemist-dev-ner
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type: combined-train-drugtemist-dev-ner
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config: CombinedTrainDrugTEMISTDevNER
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split: validation
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args: CombinedTrainDrugTEMISTDevNER
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metrics:
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- name: Precision
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type: precision
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value: 0.
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- name: Recall
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type: recall
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value: 0.
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- name: F1
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type: f1
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value: 0.
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- name: Accuracy
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type: accuracy
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value: 0.
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# output
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This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the combined-train-drugtemist-dev-ner dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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- Accuracy: 0.
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## Model description
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license: apache-2.0
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base_model: PlanTL-GOB-ES/bsc-bio-ehr-es
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tags:
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- token-classification
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- generated_from_trainer
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datasets:
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- Rodrigo1771/combined-train-drugtemist-dev-ner
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metrics:
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- precision
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- recall
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name: Token Classification
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type: token-classification
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dataset:
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name: Rodrigo1771/combined-train-drugtemist-dev-ner
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type: Rodrigo1771/combined-train-drugtemist-dev-ner
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config: CombinedTrainDrugTEMISTDevNER
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split: validation
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args: CombinedTrainDrugTEMISTDevNER
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metrics:
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- name: Precision
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type: precision
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value: 0.09532555790247038
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- name: Recall
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type: recall
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value: 0.9540441176470589
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- name: F1
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type: f1
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value: 0.17333222008850296
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- name: Accuracy
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type: accuracy
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value: 0.7932840841995413
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# output
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This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the Rodrigo1771/combined-train-drugtemist-dev-ner dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0503
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- Precision: 0.0953
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- Recall: 0.9540
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- F1: 0.1733
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- Accuracy: 0.7933
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## Model description
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all_results.json
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eval_results.json
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predict_results.json
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tb/events.out.tfevents.1725049039.6b97e535edda.13440.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:f105690f828d701992cc9bf50b2d6c540b9476f5cd4584b9d782e2db879b27e7
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size 560
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train.log
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1553 |
{'eval_loss': 1.4861844778060913, 'eval_precision': 0.0914341567442687, 'eval_recall': 0.9457720588235294, 'eval_f1': 0.16674769081186194, 'eval_accuracy': 0.7820794485561674, 'eval_runtime': 15.3988, 'eval_samples_per_second': 442.243, 'eval_steps_per_second': 55.329, 'epoch': 9.99}
|
1554 |
{'train_runtime': 1208.2019, 'train_samples_per_second': 225.368, 'train_steps_per_second': 3.518, 'train_loss': 0.10639642311544979, 'epoch': 9.99}
|
1555 |
|
1556 |
+
***** train metrics *****
|
1557 |
+
epoch = 9.9882
|
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+
total_flos = 11781054GF
|
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+
train_loss = 0.1064
|
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+
train_runtime = 0:20:08.20
|
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train_samples = 27229
|
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train_samples_per_second = 225.368
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train_steps_per_second = 3.518
|
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+
08/30/2024 20:17:04 - INFO - __main__ - *** Evaluate ***
|
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+
[INFO|trainer.py:805] 2024-08-30 20:17:04,325 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
|
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[INFO|trainer.py:3788] 2024-08-30 20:17:04,327 >>
|
1567 |
+
***** Running Evaluation *****
|
1568 |
+
[INFO|trainer.py:3790] 2024-08-30 20:17:04,327 >> Num examples = 6810
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[INFO|trainer.py:3793] 2024-08-30 20:17:04,327 >> Batch size = 8
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99%|█████████▉| 845/852 [00:10<00:00, 79.78it/s]/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
|
1674 |
+
_warn_prf(average, modifier, msg_start, len(result))
|
1675 |
+
|
1676 |
+
***** eval metrics *****
|
1677 |
+
epoch = 9.9882
|
1678 |
+
eval_accuracy = 0.7933
|
1679 |
+
eval_f1 = 0.1733
|
1680 |
+
eval_loss = 1.0503
|
1681 |
+
eval_precision = 0.0953
|
1682 |
+
eval_recall = 0.954
|
1683 |
+
eval_runtime = 0:00:14.68
|
1684 |
+
eval_samples = 6810
|
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+
eval_samples_per_second = 463.735
|
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+
eval_steps_per_second = 58.018
|
1687 |
+
08/30/2024 20:17:19 - INFO - __main__ - *** Predict ***
|
1688 |
+
[INFO|trainer.py:805] 2024-08-30 20:17:19,020 >> The following columns in the test set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
|
1689 |
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[INFO|trainer.py:3788] 2024-08-30 20:17:19,022 >>
|
1690 |
+
***** Running Prediction *****
|
1691 |
+
[INFO|trainer.py:3790] 2024-08-30 20:17:19,022 >> Num examples = 14614
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[INFO|trainer.py:3793] 2024-08-30 20:17:19,023 >> Batch size = 8
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+
[INFO|trainer.py:3478] 2024-08-30 20:17:49,537 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
|
1906 |
+
[INFO|configuration_utils.py:472] 2024-08-30 20:17:49,539 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
|
1907 |
+
[INFO|modeling_utils.py:2690] 2024-08-30 20:17:50,922 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
|
1908 |
+
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:17:50,923 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
|
1909 |
+
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:17:50,923 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
|
1910 |
+
***** predict metrics *****
|
1911 |
+
predict_accuracy = 0.8809
|
1912 |
+
predict_f1 = 0.2408
|
1913 |
+
predict_loss = 0.6289
|
1914 |
+
predict_precision = 0.1378
|
1915 |
+
predict_recall = 0.9528
|
1916 |
+
predict_runtime = 0:00:29.87
|
1917 |
+
predict_samples_per_second = 489.229
|
1918 |
+
predict_steps_per_second = 61.162
|
1919 |
+
|
train_results.json
CHANGED
@@ -2,8 +2,8 @@
|
|
2 |
"epoch": 9.988249118683902,
|
3 |
"total_flos": 1.2649810588547778e+16,
|
4 |
"train_loss": 0.10639642311544979,
|
5 |
-
"train_runtime":
|
6 |
"train_samples": 27229,
|
7 |
-
"train_samples_per_second": 225.
|
8 |
-
"train_steps_per_second": 3.
|
9 |
}
|
|
|
2 |
"epoch": 9.988249118683902,
|
3 |
"total_flos": 1.2649810588547778e+16,
|
4 |
"train_loss": 0.10639642311544979,
|
5 |
+
"train_runtime": 1208.2019,
|
6 |
"train_samples": 27229,
|
7 |
+
"train_samples_per_second": 225.368,
|
8 |
+
"train_steps_per_second": 3.518
|
9 |
}
|
trainer_state.json
CHANGED
@@ -1,5 +1,5 @@
|
|
1 |
{
|
2 |
-
"best_metric": 0.
|
3 |
"best_model_checkpoint": "/content/dissertation/scripts/ner/output/checkpoint-2127",
|
4 |
"epoch": 9.988249118683902,
|
5 |
"eval_steps": 500,
|
@@ -10,14 +10,14 @@
|
|
10 |
"log_history": [
|
11 |
{
|
12 |
"epoch": 0.9988249118683902,
|
13 |
-
"eval_accuracy": 0.
|
14 |
-
"eval_f1": 0.
|
15 |
-
"eval_loss": 0.
|
16 |
-
"eval_precision": 0.
|
17 |
-
"eval_recall": 0.
|
18 |
-
"eval_runtime": 14.
|
19 |
-
"eval_samples_per_second":
|
20 |
-
"eval_steps_per_second": 57.
|
21 |
"step": 425
|
22 |
},
|
23 |
{
|
@@ -29,14 +29,14 @@
|
|
29 |
},
|
30 |
{
|
31 |
"epoch": 2.0,
|
32 |
-
"eval_accuracy": 0.
|
33 |
-
"eval_f1": 0.
|
34 |
-
"eval_loss": 0.
|
35 |
-
"eval_precision": 0.
|
36 |
-
"eval_recall": 0.
|
37 |
-
"eval_runtime": 14.
|
38 |
-
"eval_samples_per_second":
|
39 |
-
"eval_steps_per_second": 57.
|
40 |
"step": 851
|
41 |
},
|
42 |
{
|
@@ -48,14 +48,14 @@
|
|
48 |
},
|
49 |
{
|
50 |
"epoch": 2.99882491186839,
|
51 |
-
"eval_accuracy": 0.
|
52 |
-
"eval_f1": 0.
|
53 |
-
"eval_loss": 0.
|
54 |
-
"eval_precision": 0.
|
55 |
-
"eval_recall": 0.
|
56 |
-
"eval_runtime": 14.
|
57 |
-
"eval_samples_per_second":
|
58 |
-
"eval_steps_per_second": 57.
|
59 |
"step": 1276
|
60 |
},
|
61 |
{
|
@@ -67,14 +67,14 @@
|
|
67 |
},
|
68 |
{
|
69 |
"epoch": 4.0,
|
70 |
-
"eval_accuracy": 0.
|
71 |
-
"eval_f1": 0.
|
72 |
-
"eval_loss":
|
73 |
-
"eval_precision": 0.
|
74 |
-
"eval_recall": 0.
|
75 |
-
"eval_runtime": 14.
|
76 |
-
"eval_samples_per_second":
|
77 |
-
"eval_steps_per_second": 57.
|
78 |
"step": 1702
|
79 |
},
|
80 |
{
|
@@ -86,14 +86,14 @@
|
|
86 |
},
|
87 |
{
|
88 |
"epoch": 4.9988249118683905,
|
89 |
-
"eval_accuracy": 0.
|
90 |
-
"eval_f1": 0.
|
91 |
-
"eval_loss":
|
92 |
-
"eval_precision": 0.
|
93 |
-
"eval_recall": 0.
|
94 |
-
"eval_runtime": 14.
|
95 |
-
"eval_samples_per_second": 460.
|
96 |
-
"eval_steps_per_second": 57.
|
97 |
"step": 2127
|
98 |
},
|
99 |
{
|
@@ -105,26 +105,26 @@
|
|
105 |
},
|
106 |
{
|
107 |
"epoch": 6.0,
|
108 |
-
"eval_accuracy": 0.
|
109 |
-
"eval_f1": 0.
|
110 |
-
"eval_loss":
|
111 |
-
"eval_precision": 0.
|
112 |
-
"eval_recall": 0.
|
113 |
-
"eval_runtime": 14.
|
114 |
-
"eval_samples_per_second":
|
115 |
-
"eval_steps_per_second":
|
116 |
"step": 2553
|
117 |
},
|
118 |
{
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