add model

README.md

@@ -21,7 +21,7 @@ model_index:
     metric:
       name: Accuracy
       type: accuracy
-      value: 0.5633802816901409
+      value: 0.43661971830985913
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,8 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE WNLI dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6898
-- Accuracy: 0.5634
+- Loss: 0.7083
+- Accuracy: 0.4366
 
 ## Model description
 
@@ -52,7 +52,7 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 16
+- train_batch_size: 32
 - eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

all_results.json

@@ -1,14 +1,14 @@
 {
     "epoch": 4.0,
-    "eval_accuracy": 0.5633802816901409,
-    "eval_loss": 0.6897721290588379,
-    "eval_runtime": 0.3776,
+    "eval_accuracy": 0.43661971830985913,
+    "eval_loss": 0.7083150744438171,
+    "eval_runtime": 0.3695,
     "eval_samples": 71,
-    "eval_samples_per_second": 188.013,
-    "eval_steps_per_second": 23.833,
-    "train_loss": 0.7106883525848389,
-    "train_runtime": 30.7545,
+    "eval_samples_per_second": 192.154,
+    "eval_steps_per_second": 24.358,
+    "train_loss": 0.7118758201599121,
+    "train_runtime": 29.8471,
     "train_samples": 635,
-    "train_samples_per_second": 82.59,
-    "train_steps_per_second": 5.202
+    "train_samples_per_second": 85.1,
+    "train_steps_per_second": 2.68
 }

eval_results.json

@@ -1,9 +1,9 @@
 {
     "epoch": 4.0,
-    "eval_accuracy": 0.5633802816901409,
-    "eval_loss": 0.6897721290588379,
-    "eval_runtime": 0.3776,
+    "eval_accuracy": 0.43661971830985913,
+    "eval_loss": 0.7083150744438171,
+    "eval_runtime": 0.3695,
     "eval_samples": 71,
-    "eval_samples_per_second": 188.013,
-    "eval_steps_per_second": 23.833
+    "eval_samples_per_second": 192.154,
+    "eval_steps_per_second": 24.358
 }

pytorch_model.bin

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train_results.json

@@ -1,8 +1,8 @@
 {
     "epoch": 4.0,
-    "train_loss": 0.7106883525848389,
-    "train_runtime": 30.7545,
+    "train_loss": 0.7118758201599121,
+    "train_runtime": 29.8471,
     "train_samples": 635,
-    "train_samples_per_second": 82.59,
-    "train_steps_per_second": 5.202
+    "train_samples_per_second": 85.1,
+    "train_steps_per_second": 2.68
 }

trainer_state.json

@@ -2,22 +2,22 @@
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   "best_model_checkpoint": null,
   "epoch": 4.0,
-  "global_step": 160,
+  "global_step": 80,
   "is_hyper_param_search": false,
   "is_local_process_zero": true,
   "is_world_process_zero": true,
   "log_history": [
     {
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-      "train_runtime": 30.7545,
-      "train_samples_per_second": 82.59,
-      "train_steps_per_second": 5.202
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+      "train_runtime": 29.8471,
+      "train_samples_per_second": 85.1,
+      "train_steps_per_second": 2.68
     }
   ],
-  "max_steps": 160,
+  "max_steps": 80,
   "num_train_epochs": 4,
   "total_flos": 15175294617600.0,
   "trial_name": null,

training_args.bin

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