Model save

README.md

@@ -2,11 +2,7 @@
 license: apache-2.0
 base_model: bert-base-uncased
 tags:
-- 'biology '
-- NLP
-- text-classification
-- drugs
-- BERT
+- generated_from_trainer
 metrics:
 - accuracy
 - precision
@@ -15,39 +11,34 @@ metrics:
 model-index:
 - name: bert-drug-review-to-condition
   results: []
-language:
-- en
-library_name: transformers
-datasets:
-- Zakia/drugscom_reviews
 ---
 
+<!-- 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. -->
+
 # bert-drug-review-to-condition
 
 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4308
-- Accuracy: 0.9209
-- Precision: 0.9061
-- Recall: 0.9209
-- F1: 0.9106
+- Loss: 0.6678
+- Accuracy: 0.8376
+- Precision: 0.8325
+- Recall: 0.8376
+- F1: 0.8317
 
 ## Model description
 
-Fine-tuning of Bert model with drug-related data for the purpose of text classification
+More information needed
 
 ## Intended uses & limitations
 
-Personal project.
+More information needed
 
 ## Training and evaluation data
 
-Kallumadi,Surya and Grer,Felix. (2018). Drug Reviews (Drugs.com). UCI Machine Learning Repository. https://doi.org/10.24432/C5SK5S.
+More information needed
 
 ## Training procedure
-Multiclass classification
-The model predicts the 'condition' feature from the 'review' feature, only the first 21 conditions are selected.
-The 'review' feature is lowercased, we select only values with at least 16 characters.
 
 ### Training hyperparameters
 
@@ -62,11 +53,11 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| No log        | 1.0   | 113  | 1.1375          | 0.7747   | 0.7301    | 0.7747 | 0.7450 |
-| No log        | 2.0   | 226  | 0.5595          | 0.8854   | 0.8675    | 0.8854 | 0.8728 |
-| No log        | 3.0   | 339  | 0.4308          | 0.9209   | 0.9061    | 0.9209 | 0.9106 |
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.8469        | 1.0   | 13390 | 0.8275          | 0.7673   | 0.7686    | 0.7673 | 0.7551 |
+| 0.6319        | 2.0   | 26780 | 0.6895          | 0.8094   | 0.8090    | 0.8094 | 0.7978 |
+| 0.4116        | 3.0   | 40170 | 0.6678          | 0.8376   | 0.8325    | 0.8376 | 0.8317 |
 
 
 ### Framework versions
@@ -74,4 +65,4 @@ The following hyperparameters were used during training:
 - Transformers 4.40.0
 - Pytorch 2.2.1+cu121
 - Datasets 2.19.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1