End of training

README.md

@@ -1,24 +1,84 @@
 ---
-license: mit
-base_model: FacebookAI/roberta-large
+library_name: transformers
 tags:
 - generated_from_trainer
-- medical
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
-- name: roberta-ner
+- name: medical-ner-roberta
   results: []
-widget:
-- text: 63 year old woman with history of CAD presented to ER
-  example_title: Example-1
-- text: 63 year old woman diagnosed with CAD
-  example_title: Example-2
-- text: >-
-    A 48 year-old female presented with vaginal bleeding and abnormal Pap
-    smears. Upon diagnosis of invasive non-keratinizing SCC of the cervix, she
-    underwent a radical hysterectomy with salpingo-oophorectomy which
-    demonstrated positive spread to the pelvic lymph nodes and the parametrium.
-    Pathological examination revealed that the tumour also extensively involved
-    the lower uterine segment.
-  example_title: example 3
-pipeline_tag: token-classification
----
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# medical-ner-roberta
+
+This model was trained from scratch on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1293
+- Precision: 0.9306
+- Recall: 0.9431
+- F1: 0.9368
+- Accuracy: 0.9792
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 90   | 0.6883          | 0.4376    | 0.4556 | 0.4464 | 0.7834   |
+| No log        | 2.0   | 180  | 0.4971          | 0.5779    | 0.6286 | 0.6022 | 0.8343   |
+| No log        | 3.0   | 270  | 0.4184          | 0.5892    | 0.7451 | 0.6581 | 0.8569   |
+| No log        | 4.0   | 360  | 0.3410          | 0.6474    | 0.8062 | 0.7182 | 0.8893   |
+| No log        | 5.0   | 450  | 0.2515          | 0.7554    | 0.8181 | 0.7855 | 0.9270   |
+| 0.5383        | 6.0   | 540  | 0.2256          | 0.7738    | 0.8577 | 0.8136 | 0.9338   |
+| 0.5383        | 7.0   | 630  | 0.1782          | 0.8270    | 0.8824 | 0.8538 | 0.9488   |
+| 0.5383        | 8.0   | 720  | 0.1734          | 0.8271    | 0.8977 | 0.8610 | 0.9554   |
+| 0.5383        | 9.0   | 810  | 0.1474          | 0.8702    | 0.9123 | 0.8908 | 0.9661   |
+| 0.5383        | 10.0  | 900  | 0.1476          | 0.8806    | 0.9216 | 0.9006 | 0.9685   |
+| 0.5383        | 11.0  | 990  | 0.1404          | 0.8913    | 0.9304 | 0.9105 | 0.9722   |
+| 0.0733        | 12.0  | 1080 | 0.1354          | 0.9085    | 0.9273 | 0.9178 | 0.9741   |
+| 0.0733        | 13.0  | 1170 | 0.1332          | 0.9112    | 0.9266 | 0.9188 | 0.9739   |
+| 0.0733        | 14.0  | 1260 | 0.1337          | 0.9072    | 0.9396 | 0.9231 | 0.9755   |
+| 0.0733        | 15.0  | 1350 | 0.1332          | 0.9283    | 0.9362 | 0.9322 | 0.9776   |
+| 0.0733        | 16.0  | 1440 | 0.1293          | 0.9321    | 0.9389 | 0.9355 | 0.9783   |
+| 0.0236        | 17.0  | 1530 | 0.1307          | 0.9253    | 0.9431 | 0.9341 | 0.9786   |
+| 0.0236        | 18.0  | 1620 | 0.1293          | 0.9278    | 0.9439 | 0.9358 | 0.9788   |
+| 0.0236        | 19.0  | 1710 | 0.1294          | 0.9306    | 0.9431 | 0.9368 | 0.9792   |
+| 0.0236        | 20.0  | 1800 | 0.1293          | 0.9306    | 0.9431 | 0.9368 | 0.9792   |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

runs/Nov17_19-06-42_46bb810912a7/events.out.tfevents.1731873114.46bb810912a7.361.1

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