update model card README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: allenai/scibert_scivocab_uncased
+base_model: allenai/longformer-large-4096
 tags:
 - generated_from_trainer
 metrics:
@@ -17,13 +17,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # patentClassfication2
 
-This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
+This model is a fine-tuned version of [allenai/longformer-large-4096](https://huggingface.co/allenai/longformer-large-4096) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6329
-- Accuracy: 0.6513
-- F1: 0.6099
-- Precision: 0.6941
-- Recall: 0.5438
+- Loss: 0.6395
+- Accuracy: 0.645
+- F1: 0.6764
+- Precision: 0.6214
+- Recall: 0.742
 
 ## Model description
 
@@ -42,23 +42,24 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2.54241e-05
+- learning_rate: 7.224533222600416e-06
 - train_batch_size: 8
 - eval_batch_size: 8
-- seed: 41
+- seed: 3
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_ratio: 0.1
-- lr_scheduler_warmup_steps: 24
-- num_epochs: 3
+- lr_scheduler_warmup_steps: 371
+- num_epochs: 4
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 0.635         | 1.0   | 4438  | 0.6329          | 0.6513   | 0.6099 | 0.6941    | 0.5438 |
-| 0.5772        | 2.0   | 8876  | 0.6393          | 0.6721   | 0.6831 | 0.6624    | 0.7050 |
-| 0.5355        | 3.0   | 13314 | 0.6558          | 0.6683   | 0.6768 | 0.6613    | 0.6931 |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.6917        | 1.0   | 500  | 0.6714          | 0.595    | 0.64   | 0.576     | 0.72   |
+| 0.6577        | 2.0   | 1000 | 0.6395          | 0.645    | 0.6764 | 0.6214    | 0.742  |
+| 0.536         | 3.0   | 1500 | 0.6535          | 0.675    | 0.6531 | 0.7002    | 0.612  |
+| 0.4025        | 4.0   | 2000 | 0.6808          | 0.686    | 0.6879 | 0.6838    | 0.692  |
 
 
 ### Framework versions