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-fasttext-75-ner
+- drugtemist-en-fasttext-8-ner
 metrics:
 - precision
 - recall
@@ -19,24 +18,24 @@ model-index:
       name: Token Classification
       type: token-classification
     dataset:
-      name: Rodrigo1771/drugtemist-en-fasttext-75-ner
-      type: Rodrigo1771/drugtemist-en-fasttext-75-ner
+      name: drugtemist-en-fasttext-8-ner
+      type: drugtemist-en-fasttext-8-ner
       config: DrugTEMIST English NER
       split: validation
       args: DrugTEMIST English NER
     metrics:
     - name: Precision
       type: precision
-      value: 0.9249771271729186
+      value: 0.9247015610651974
     - name: Recall
       type: recall
-      value: 0.9422180801491147
+      value: 0.9384902143522833
     - name: F1
       type: f1
-      value: 0.9335180055401663
+      value: 0.9315448658649399
     - name: Accuracy
       type: accuracy
-      value: 0.998772081600759
+      value: 0.9987092903189797
 ---
 
 <!-- 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-fasttext-75-ner dataset.
+This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the drugtemist-en-fasttext-8-ner dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0076
-- Precision: 0.9250
-- Recall: 0.9422
-- F1: 0.9335
-- Accuracy: 0.9988
+- Loss: 0.0079
+- Precision: 0.9247
+- Recall: 0.9385
+- F1: 0.9315
+- Accuracy: 0.9987
 
 ## Model description
 
@@ -81,18 +80,18 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0183        | 1.0   | 507  | 0.0055          | 0.8974    | 0.9376 | 0.9170 | 0.9985   |
-| 0.0043        | 2.0   | 1014 | 0.0059          | 0.9099    | 0.9320 | 0.9208 | 0.9986   |
-| 0.0022        | 3.0   | 1521 | 0.0057          | 0.9015    | 0.9301 | 0.9156 | 0.9985   |
-| 0.0018        | 4.0   | 2028 | 0.0072          | 0.9275    | 0.9180 | 0.9227 | 0.9986   |
-| 0.0009        | 5.0   | 2535 | 0.0064          | 0.9078    | 0.9357 | 0.9215 | 0.9987   |
-| 0.0007        | 6.0   | 3042 | 0.0064          | 0.9194    | 0.9357 | 0.9275 | 0.9987   |
-| 0.0004        | 7.0   | 3549 | 0.0072          | 0.9289    | 0.9376 | 0.9332 | 0.9988   |
-| 0.0004        | 8.0   | 4056 | 0.0076          | 0.9250    | 0.9422 | 0.9335 | 0.9988   |
-| 0.0003        | 9.0   | 4563 | 0.0077          | 0.9161    | 0.9366 | 0.9263 | 0.9987   |
-| 0.0002        | 10.0  | 5070 | 0.0077          | 0.9195    | 0.9366 | 0.9280 | 0.9988   |
+| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 0.9990 | 481  | 0.0042          | 0.9173    | 0.9413 | 0.9292 | 0.9987   |
+| 0.0156        | 2.0    | 963  | 0.0049          | 0.9134    | 0.9245 | 0.9189 | 0.9986   |
+| 0.0039        | 2.9990 | 1444 | 0.0053          | 0.8914    | 0.9487 | 0.9192 | 0.9986   |
+| 0.0024        | 4.0    | 1926 | 0.0061          | 0.8820    | 0.9543 | 0.9167 | 0.9985   |
+| 0.0017        | 4.9990 | 2407 | 0.0074          | 0.9199    | 0.9310 | 0.9254 | 0.9986   |
+| 0.0011        | 6.0    | 2889 | 0.0079          | 0.9170    | 0.9366 | 0.9267 | 0.9986   |
+| 0.0007        | 6.9990 | 3370 | 0.0067          | 0.9092    | 0.9422 | 0.9254 | 0.9987   |
+| 0.0005        | 8.0    | 3852 | 0.0073          | 0.9249    | 0.9301 | 0.9275 | 0.9987   |
+| 0.0004        | 8.9990 | 4333 | 0.0080          | 0.9272    | 0.9376 | 0.9323 | 0.9987   |
+| 0.0002        | 9.9896 | 4810 | 0.0079          | 0.9247    | 0.9385 | 0.9315 | 0.9987   |
 
 
 ### Framework versions

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:11ef9ef0b3ffe448b5d336cfe84f29a6a9a854b52a6e238ebd9389de82be1947
+oid sha256:c9da95e97df509d5e58adf7ec957e86d1d95cf632581ad3f251e6daa7df49c2a
 size 430601004

tb/events.out.tfevents.1725888716.0a1c9bec2a53.34821.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fda94b3ba089098406124a49f77563fbcc7625629fecf4a02a9057bb530a6a7c
-size 11268
+oid sha256:90e8e7961730149c6c235f6c85cd7c14fbfd2b133f0d765a77e4ff36d922e037
+size 12094

train.log

@@ -1417,3 +1417,16 @@ Training completed. Do not forget to share your model on huggingface.co/models =
 [INFO|trainer.py:2632] 2024-09-09 14:06:41,207 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-4333 (score: 0.9323447636700648).
 
                                                   
 [INFO|trainer.py:4283] 2024-09-09 14:06:41,378 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
+[INFO|trainer.py:3503] 2024-09-09 14:07:13,448 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
+[INFO|configuration_utils.py:472] 2024-09-09 14:07:13,449 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
+[INFO|modeling_utils.py:2799] 2024-09-09 14:07:14,576 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
+[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:07:14,577 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:07:14,577 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
+[INFO|trainer.py:3503] 2024-09-09 14:07:14,590 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
+[INFO|configuration_utils.py:472] 2024-09-09 14:07:14,591 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
+[INFO|modeling_utils.py:2799] 2024-09-09 14:07:16,333 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
+[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:07:16,334 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:07:16,334 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
+{'eval_loss': 0.007857992313802242, 'eval_precision': 0.9247015610651974, 'eval_recall': 0.9384902143522833, 'eval_f1': 0.9315448658649399, 'eval_accuracy': 0.9987092903189797, 'eval_runtime': 15.1843, 'eval_samples_per_second': 457.447, 'eval_steps_per_second': 57.23, 'epoch': 9.99}
+{'train_runtime': 2084.9871, 'train_samples_per_second': 147.78, 'train_steps_per_second': 2.307, 'train_loss': 0.0027612092573652642, 'epoch': 9.99}
+