Model save

README.md

@@ -1,31 +1,20 @@
 ---
 library_name: transformers
 license: apache-2.0
-base_model: HuggingFaceTB/SmolLM2-360M
+base_model: HuggingFaceTB/SmolLM2-135M-Instruct
 tags:
 - generated_from_trainer
-metrics:
-- f1
-- accuracy
-- precision
-- recall
 model-index:
-- name: toxicity-scorer-smollm2-360m-freeze
+- name: toxicity-scorer-smollm2-135m-it-freeze
   results: []
 ---
 
 <!-- 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. -->
 
-# toxicity-scorer-smollm2-360m-freeze
+# toxicity-scorer-smollm2-135m-it-freeze
 
-This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.2347
-- F1: 0.9013
-- Accuracy: 0.9033
-- Precision: 0.9006
-- Recall: 0.9033
+This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) on an unknown dataset.
 
 ## Model description
 
@@ -44,9 +33,9 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 32
-- eval_batch_size: 32
+- learning_rate: 3e-05
+- train_batch_size: 36
+- eval_batch_size: 36
 - seed: 42
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: cosine
@@ -55,21 +44,9 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch  | Step  | Validation Loss | F1     | Accuracy | Precision | Recall |
-|:-------------:|:------:|:-----:|:---------------:|:------:|:--------:|:---------:|:------:|
-| No log        | 0      | 0     | 0.8130          | 0.5487 | 0.5049   | 0.6645    | 0.5049 |
-| 0.2957        | 0.2340 | 5000  | 0.2896          | 0.8803 | 0.8841   | 0.8795    | 0.8841 |
-| 0.2451        | 0.4680 | 10000 | 0.2443          | 0.8976 | 0.8995   | 0.8968    | 0.8995 |
-| 0.2349        | 0.7020 | 15000 | 0.2383          | 0.8994 | 0.9020   | 0.8989    | 0.9020 |
-| 0.2277        | 0.9360 | 20000 | 0.2363          | 0.9006 | 0.9027   | 0.8999    | 0.9027 |
-| 0.2414        | 1.1700 | 25000 | 0.2352          | 0.9013 | 0.9035   | 0.9007    | 0.9035 |
-| 0.2361        | 1.4040 | 30000 | 0.2349          | 0.9013 | 0.9035   | 0.9007    | 0.9035 |
-| 0.2312        | 1.6380 | 35000 | 0.2348          | 0.9013 | 0.9033   | 0.9007    | 0.9033 |
-| 0.2207        | 1.8720 | 40000 | 0.2348          | 0.9014 | 0.9035   | 0.9007    | 0.9035 |
-| 0.2645        | 2.1060 | 45000 | 0.2347          | 0.9012 | 0.9033   | 0.9005    | 0.9033 |
-| 0.2369        | 2.3399 | 50000 | 0.2347          | 0.9012 | 0.9033   | 0.9005    | 0.9033 |
-| 0.2329        | 2.5739 | 55000 | 0.2347          | 0.9013 | 0.9034   | 0.9006    | 0.9034 |
-| 0.2253        | 2.8079 | 60000 | 0.2347          | 0.9013 | 0.9033   | 0.9006    | 0.9033 |
+| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|
+| No log        | 0     | 0    | 0.9096          | 0.5932 | 0.507    | 0.7400    | 0.507  |
 
 
 ### Framework versions