Model save

README.md

@@ -3,10 +3,9 @@ library_name: transformers
 license: apache-2.0
 base_model: michiyasunaga/BioLinkBERT-base
 tags:
-- token-classification
 - generated_from_trainer
 datasets:
-- Rodrigo1771/drugtemist-en-75-ner
+- drugtemist-en-8-ner
 metrics:
 - precision
 - recall
@@ -19,24 +18,24 @@ model-index:
       name: Token Classification
       type: token-classification
     dataset:
-      name: Rodrigo1771/drugtemist-en-75-ner
-      type: Rodrigo1771/drugtemist-en-75-ner
+      name: drugtemist-en-8-ner
+      type: drugtemist-en-8-ner
       config: DrugTEMIST English NER
       split: validation
       args: DrugTEMIST English NER
     metrics:
     - name: Precision
       type: precision
-      value: 0.9342105263157895
+      value: 0.9172033118675254
     - name: Recall
       type: recall
-      value: 0.9263746505125815
+      value: 0.9291705498602051
     - name: F1
       type: f1
-      value: 0.930276087973795
+      value: 0.9231481481481483
     - name: Accuracy
       type: accuracy
-      value: 0.9987162671280663
+      value: 0.9985418469009014
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,13 +43,13 @@ 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-75-ner dataset.
+This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the drugtemist-en-8-ner dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0065
-- Precision: 0.9342
-- Recall: 0.9264
-- F1: 0.9303
-- Accuracy: 0.9987
+- Loss: 0.0089
+- Precision: 0.9172
+- Recall: 0.9292
+- F1: 0.9231
+- Accuracy: 0.9985
 
 ## Model description
 
@@ -83,16 +82,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0189        | 1.0   | 504  | 0.0052          | 0.8712    | 0.9394 | 0.9040 | 0.9984   |
-| 0.0047        | 2.0   | 1008 | 0.0048          | 0.9253    | 0.9236 | 0.9244 | 0.9987   |
-| 0.0027        | 3.0   | 1512 | 0.0059          | 0.9252    | 0.9226 | 0.9239 | 0.9986   |
-| 0.0015        | 4.0   | 2016 | 0.0065          | 0.9342    | 0.9264 | 0.9303 | 0.9987   |
-| 0.0011        | 5.0   | 2520 | 0.0073          | 0.9073    | 0.9394 | 0.9231 | 0.9986   |
-| 0.0005        | 6.0   | 3024 | 0.0090          | 0.9191    | 0.9217 | 0.9204 | 0.9984   |
-| 0.0007        | 7.0   | 3528 | 0.0084          | 0.9074    | 0.9310 | 0.9190 | 0.9986   |
-| 0.0004        | 8.0   | 4032 | 0.0085          | 0.9093    | 0.9338 | 0.9214 | 0.9986   |
-| 0.0003        | 9.0   | 4536 | 0.0080          | 0.9186    | 0.9357 | 0.9271 | 0.9987   |
-| 0.0002        | 10.0  | 5040 | 0.0083          | 0.9210    | 0.9348 | 0.9278 | 0.9987   |
+| No log        | 1.0   | 493  | 0.0050          | 0.9288    | 0.9245 | 0.9267 | 0.9987   |
+| 0.018         | 2.0   | 986  | 0.0057          | 0.9104    | 0.9189 | 0.9147 | 0.9984   |
+| 0.0044        | 3.0   | 1479 | 0.0079          | 0.9362    | 0.9161 | 0.9260 | 0.9985   |
+| 0.0023        | 4.0   | 1972 | 0.0057          | 0.9318    | 0.9301 | 0.9310 | 0.9987   |
+| 0.0014        | 5.0   | 2465 | 0.0070          | 0.9201    | 0.9226 | 0.9214 | 0.9986   |
+| 0.0008        | 6.0   | 2958 | 0.0082          | 0.9118    | 0.9254 | 0.9186 | 0.9985   |
+| 0.0006        | 7.0   | 3451 | 0.0074          | 0.9172    | 0.9394 | 0.9282 | 0.9986   |
+| 0.0003        | 8.0   | 3944 | 0.0085          | 0.9219    | 0.9245 | 0.9232 | 0.9985   |
+| 0.0003        | 9.0   | 4437 | 0.0086          | 0.9149    | 0.9320 | 0.9234 | 0.9985   |
+| 0.0002        | 10.0  | 4930 | 0.0089          | 0.9172    | 0.9292 | 0.9231 | 0.9985   |
 
 
 ### Framework versions

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cb1ef42300d9e3fe8df4b979fcc05f0ee5e712f35d3020950e015e2408591c1e
+oid sha256:f0e1906e22e1e74ba71629d5fba7177183dc796679b0ec27976f490ac5ac9c44
 size 430601004

tb/events.out.tfevents.1725528054.6cb9bed92fd1.10874.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:de23eae6be5b88fd6826f8a03ba329abd3f0071dceaf3bd1465d0456b13359e4
-size 11268
+oid sha256:287148fc9a2a755f0eb05d0bf65de2fca23a1e7fc7b944e80a7cb18035edf940
+size 12094

train.log

@@ -1267,3 +1267,16 @@ Training completed. Do not forget to share your model on huggingface.co/models =
 [INFO|trainer.py:2632] 2024-09-05 09:41:16,700 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-1972 (score: 0.9309701492537313).
 
                                                   
 [INFO|trainer.py:4283] 2024-09-05 09:41:16,869 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
+[INFO|trainer.py:3503] 2024-09-05 09:41:23,447 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
+[INFO|configuration_utils.py:472] 2024-09-05 09:41:23,449 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
+[INFO|modeling_utils.py:2799] 2024-09-05 09:41:24,716 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
+[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:41:24,717 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:41:24,717 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
+[INFO|trainer.py:3503] 2024-09-05 09:41:24,730 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
+[INFO|configuration_utils.py:472] 2024-09-05 09:41:24,732 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
+[INFO|modeling_utils.py:2799] 2024-09-05 09:41:26,453 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
+[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:41:26,454 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:41:26,454 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
+{'eval_loss': 0.008874327875673771, 'eval_precision': 0.9172033118675254, 'eval_recall': 0.9291705498602051, 'eval_f1': 0.9231481481481483, 'eval_accuracy': 0.9985418469009014, 'eval_runtime': 13.6739, 'eval_samples_per_second': 507.975, 'eval_steps_per_second': 63.552, 'epoch': 10.0}
+{'train_runtime': 1222.2462, 'train_samples_per_second': 258.017, 'train_steps_per_second': 4.034, 'train_loss': 0.0028862289850114567, 'epoch': 10.0}
+