Model save

README.md

@@ -1,11 +1,9 @@
 ---
-license: apache-2.0
-base_model: michiyasunaga/BioLinkBERT-base
+base_model: IVN-RIN/bioBIT
 tags:
-- token-classification
 - generated_from_trainer
 datasets:
-- Rodrigo1771/drugtemist-en-ner
+- drugtemist-it-ner
 metrics:
 - precision
 - recall
@@ -18,24 +16,24 @@ model-index:
       name: Token Classification
       type: token-classification
     dataset:
-      name: Rodrigo1771/drugtemist-en-ner
-      type: Rodrigo1771/drugtemist-en-ner
-      config: DrugTEMIST English NER
+      name: drugtemist-it-ner
+      type: drugtemist-it-ner
+      config: DrugTEMIST Italian NER
       split: validation
-      args: DrugTEMIST English NER
+      args: DrugTEMIST Italian NER
     metrics:
     - name: Precision
       type: precision
-      value: 0.9327102803738317
+      value: 0.9328214971209213
     - name: Recall
       type: recall
-      value: 0.9301025163094129
+      value: 0.9409486931268151
     - name: F1
       type: f1
-      value: 0.9314045730284647
+      value: 0.936867469879518
     - name: Accuracy
       type: accuracy
-      value: 0.9986953367008066
+      value: 0.9988184887042326
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,13 +41,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # output
 
-This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the Rodrigo1771/drugtemist-en-ner dataset.
+This model is a fine-tuned version of [IVN-RIN/bioBIT](https://huggingface.co/IVN-RIN/bioBIT) on the drugtemist-it-ner dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0056
-- Precision: 0.9327
-- Recall: 0.9301
-- F1: 0.9314
-- Accuracy: 0.9987
+- Loss: 0.0067
+- Precision: 0.9328
+- Recall: 0.9409
+- F1: 0.9369
+- Accuracy: 0.9988
 
 ## Model description
 
@@ -82,16 +80,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 434  | 0.0057          | 0.8938    | 0.8938 | 0.8938 | 0.9981   |
-| 0.0182        | 2.0   | 868  | 0.0044          | 0.9024    | 0.9301 | 0.9160 | 0.9985   |
-| 0.0039        | 3.0   | 1302 | 0.0045          | 0.9129    | 0.9282 | 0.9205 | 0.9987   |
-| 0.0024        | 4.0   | 1736 | 0.0051          | 0.8821    | 0.9348 | 0.9077 | 0.9983   |
-| 0.0017        | 5.0   | 2170 | 0.0057          | 0.9251    | 0.9320 | 0.9285 | 0.9986   |
-| 0.0012        | 6.0   | 2604 | 0.0061          | 0.9001    | 0.9236 | 0.9117 | 0.9984   |
-| 0.0009        | 7.0   | 3038 | 0.0056          | 0.9327    | 0.9301 | 0.9314 | 0.9987   |
-| 0.0009        | 8.0   | 3472 | 0.0068          | 0.9118    | 0.9348 | 0.9231 | 0.9986   |
-| 0.0006        | 9.0   | 3906 | 0.0072          | 0.9267    | 0.9310 | 0.9289 | 0.9987   |
-| 0.0004        | 10.0  | 4340 | 0.0073          | 0.9192    | 0.9329 | 0.9260 | 0.9986   |
+| No log        | 1.0   | 425  | 0.0056          | 0.8672    | 0.9226 | 0.8940 | 0.9981   |
+| 0.0104        | 2.0   | 850  | 0.0042          | 0.9151    | 0.9284 | 0.9217 | 0.9986   |
+| 0.0034        | 3.0   | 1275 | 0.0043          | 0.9182    | 0.9129 | 0.9155 | 0.9985   |
+| 0.0022        | 4.0   | 1700 | 0.0044          | 0.9365    | 0.9138 | 0.9250 | 0.9986   |
+| 0.0012        | 5.0   | 2125 | 0.0061          | 0.9107    | 0.9284 | 0.9195 | 0.9985   |
+| 0.0009        | 6.0   | 2550 | 0.0060          | 0.9104    | 0.9342 | 0.9221 | 0.9987   |
+| 0.0009        | 7.0   | 2975 | 0.0065          | 0.9230    | 0.9400 | 0.9314 | 0.9987   |
+| 0.0005        | 8.0   | 3400 | 0.0059          | 0.9258    | 0.9303 | 0.9281 | 0.9987   |
+| 0.0004        | 9.0   | 3825 | 0.0066          | 0.9255    | 0.9380 | 0.9317 | 0.9987   |
+| 0.0001        | 10.0  | 4250 | 0.0067          | 0.9328    | 0.9409 | 0.9369 | 0.9988   |
 
 
 ### Framework versions

tb/events.out.tfevents.1725054888.6b97e535edda.43233.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0778b98e7a0b67111af5f3b6bd3dfdfd81d762456b8f6045a2b33aa9ec6416b1
-size 10970
+oid sha256:220dbeee6287faaf62206a0c9a0609807fa6e7c7aaed1492d44e9eed981b8117
+size 11796

train.log

@@ -1323,3 +1323,16 @@ Training completed. Do not forget to share your model on huggingface.co/models =
 [INFO|trainer.py:2621] 2024-08-30 22:15:49,390 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-4250 (score: 0.936867469879518).
 
                                                   
 [INFO|trainer.py:4239] 2024-08-30 22:15:49,547 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
+[INFO|trainer.py:3478] 2024-08-30 22:15:55,114 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
+[INFO|configuration_utils.py:472] 2024-08-30 22:15:55,165 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
+[INFO|modeling_utils.py:2690] 2024-08-30 22:15:56,339 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
+[INFO|tokenization_utils_base.py:2574] 2024-08-30 22:15:56,340 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2583] 2024-08-30 22:15:56,340 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
+[INFO|trainer.py:3478] 2024-08-30 22:15:56,353 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
+[INFO|configuration_utils.py:472] 2024-08-30 22:15:56,355 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
+[INFO|modeling_utils.py:2690] 2024-08-30 22:15:57,511 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
+[INFO|tokenization_utils_base.py:2574] 2024-08-30 22:15:57,512 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2583] 2024-08-30 22:15:57,513 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
+{'eval_loss': 0.006724909413605928, 'eval_precision': 0.9328214971209213, 'eval_recall': 0.9409486931268151, 'eval_f1': 0.936867469879518, 'eval_accuracy': 0.9988184887042326, 'eval_runtime': 14.3451, 'eval_samples_per_second': 473.891, 'eval_steps_per_second': 59.254, 'epoch': 10.0}
+{'train_runtime': 1261.5031, 'train_samples_per_second': 215.6, 'train_steps_per_second': 3.369, 'train_loss': 0.0022696754537961062, 'epoch': 10.0}
+