t5_cnn_daily_mail_abstractive_summarizer_v2

README.md

@@ -17,10 +17,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2981
-- Rouge1: 0.5957
-- Rouge2: 0.3287
-- Rougel: 0.5448
+- Loss: 0.2989
+- Rouge1: 0.5939
+- Rouge2: 0.3276
+- Rougel: 0.5432
 - Gen Len: 82.8793
 
 ## Model description
@@ -49,29 +49,20 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
-- num_epochs: 10
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Gen Len |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:-------:|
-| 0.3573        | 0.56  | 200  | 0.3390          | 0.5817 | 0.3123 | 0.5296 | 81.8915 |
-| 0.3175        | 1.11  | 400  | 0.3073          | 0.593  | 0.3249 | 0.5405 | 82.855  |
-| 0.317         | 1.67  | 600  | 0.3025          | 0.5956 | 0.3275 | 0.5434 | 82.8793 |
-| 0.2948        | 2.23  | 800  | 0.3009          | 0.5959 | 0.3275 | 0.5444 | 82.8793 |
-| 0.2895        | 2.79  | 1000 | 0.2999          | 0.5955 | 0.3286 | 0.5442 | 82.8793 |
-| 0.292         | 3.34  | 1200 | 0.2997          | 0.5951 | 0.3277 | 0.5441 | 82.8793 |
-| 0.2959        | 3.9   | 1400 | 0.2991          | 0.5962 | 0.3286 | 0.5447 | 82.8793 |
-| 0.2847        | 4.46  | 1600 | 0.2993          | 0.5955 | 0.3285 | 0.5441 | 82.8793 |
-| 0.2922        | 5.01  | 1800 | 0.2983          | 0.5957 | 0.3279 | 0.5447 | 82.8793 |
-| 0.2978        | 5.57  | 2000 | 0.2985          | 0.5963 | 0.3288 | 0.545  | 82.8793 |
-| 0.2952        | 6.13  | 2200 | 0.2985          | 0.5953 | 0.328  | 0.5442 | 82.8793 |
-| 0.2832        | 6.69  | 2400 | 0.2982          | 0.595  | 0.328  | 0.5447 | 82.8793 |
-| 0.2862        | 7.24  | 2600 | 0.2984          | 0.5954 | 0.3277 | 0.5442 | 82.8793 |
-| 0.2992        | 7.8   | 2800 | 0.2978          | 0.5958 | 0.3278 | 0.5449 | 82.8793 |
-| 0.2848        | 8.36  | 3000 | 0.2975          | 0.5952 | 0.3279 | 0.5449 | 82.8793 |
-| 0.2815        | 8.91  | 3200 | 0.2978          | 0.5962 | 0.3291 | 0.5452 | 82.8793 |
-| 0.2894        | 9.47  | 3400 | 0.2981          | 0.5957 | 0.3287 | 0.5448 | 82.8793 |
+| 0.2861        | 0.56  | 200  | 0.2978          | 0.5964 | 0.3284 | 0.5453 | 82.8793 |
+| 0.2889        | 1.11  | 400  | 0.2988          | 0.5954 | 0.3284 | 0.5442 | 82.8793 |
+| 0.2936        | 1.67  | 600  | 0.2988          | 0.5947 | 0.327  | 0.5432 | 82.8793 |
+| 0.2743        | 2.23  | 800  | 0.2993          | 0.5946 | 0.3277 | 0.5442 | 82.8793 |
+| 0.271         | 2.79  | 1000 | 0.2987          | 0.5942 | 0.3279 | 0.5442 | 82.8793 |
+| 0.2742        | 3.34  | 1200 | 0.2992          | 0.5937 | 0.3274 | 0.5436 | 82.8793 |
+| 0.2795        | 3.9   | 1400 | 0.2988          | 0.5934 | 0.327  | 0.543  | 82.8793 |
+| 0.2696        | 4.46  | 1600 | 0.2989          | 0.5939 | 0.3276 | 0.5432 | 82.8793 |
 
 
 ### Framework versions

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