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# distilbert-base-uncased-finetuned-sst-2-english-finetuned-Multi_classification

This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6930
- Accuracy: 0.2680
- Macro Averaged Precision: 0.2166
- Micro Averaged Precision: 0.2680
- Macro Averaged Recall: 0.2327
- Micro Averaged Recall: 0.2680
- Macro Averaged F1: 0.2116
- Micro Averaged F1: 0.2680

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Macro Averaged Precision | Micro Averaged Precision | Macro Averaged Recall | Micro Averaged Recall | Macro Averaged F1 | Micro Averaged F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------------------------:|:------------------------:|:---------------------:|:---------------------:|:-----------------:|:-----------------:|
| 1.7458        | 1.0   | 635   | 1.7202          | 0.25     | 0.3757                   | 0.25                     | 0.2110                | 0.25                  | 0.1614            | 0.25              |
| 1.6782        | 2.0   | 1270  | 1.6930          | 0.2680   | 0.2166                   | 0.2680                   | 0.2327                | 0.2680                | 0.2116            | 0.2680            |
| 1.5839        | 3.0   | 1905  | 1.8221          | 0.2430   | 0.2825                   | 0.2430                   | 0.2197                | 0.2430                | 0.2010            | 0.2430            |
| 1.217         | 4.0   | 2540  | 1.9127          | 0.2594   | 0.2682                   | 0.2594                   | 0.2418                | 0.2594                | 0.2450            | 0.2594            |
| 0.9503        | 5.0   | 3175  | 2.3028          | 0.2430   | 0.2555                   | 0.2430                   | 0.2231                | 0.2430                | 0.2179            | 0.2430            |
| 0.7459        | 6.0   | 3810  | 2.6594          | 0.25     | 0.2742                   | 0.25                     | 0.2328                | 0.25                  | 0.2312            | 0.25              |
| 0.5819        | 7.0   | 4445  | 3.0386          | 0.2469   | 0.2698                   | 0.2469                   | 0.2312                | 0.2469                | 0.2319            | 0.2469            |
| 0.3212        | 8.0   | 5080  | 3.3728          | 0.2547   | 0.2844                   | 0.2547                   | 0.2379                | 0.2547                | 0.2413            | 0.2547            |
| 0.2428        | 9.0   | 5715  | 3.9017          | 0.2398   | 0.2627                   | 0.2398                   | 0.2220                | 0.2398                | 0.2173            | 0.2398            |
| 0.1962        | 10.0  | 6350  | 4.2271          | 0.2492   | 0.2717                   | 0.2492                   | 0.2352                | 0.2492                | 0.2378            | 0.2492            |
| 0.1515        | 11.0  | 6985  | 4.6681          | 0.2484   | 0.2570                   | 0.2484                   | 0.2314                | 0.2484                | 0.2304            | 0.2484            |
| 0.1085        | 12.0  | 7620  | 5.3864          | 0.25     | 0.2756                   | 0.25                     | 0.2359                | 0.25                  | 0.2372            | 0.25              |
| 0.0996        | 13.0  | 8255  | 5.6783          | 0.2516   | 0.2624                   | 0.2516                   | 0.2331                | 0.2516                | 0.2304            | 0.2516            |
| 0.1114        | 14.0  | 8890  | 5.9540          | 0.2477   | 0.2734                   | 0.2477                   | 0.2338                | 0.2477                | 0.2367            | 0.2477            |
| 0.073         | 15.0  | 9525  | 6.5480          | 0.2492   | 0.2575                   | 0.2492                   | 0.2311                | 0.2492                | 0.2238            | 0.2492            |
| 0.065         | 16.0  | 10160 | 6.7862          | 0.2602   | 0.2740                   | 0.2602                   | 0.2438                | 0.2602                | 0.2434            | 0.2602            |
| 0.0636        | 17.0  | 10795 | 7.0422          | 0.2578   | 0.2806                   | 0.2578                   | 0.2359                | 0.2578                | 0.2354            | 0.2578            |
| 0.0662        | 18.0  | 11430 | 7.2309          | 0.2641   | 0.2856                   | 0.2641                   | 0.2526                | 0.2641                | 0.2588            | 0.2641            |
| 0.0582        | 19.0  | 12065 | 7.5682          | 0.2617   | 0.2797                   | 0.2617                   | 0.2435                | 0.2617                | 0.2462            | 0.2617            |
| 0.0541        | 20.0  | 12700 | 7.8098          | 0.2531   | 0.2711                   | 0.2531                   | 0.2344                | 0.2531                | 0.2338            | 0.2531            |
| 0.0511        | 21.0  | 13335 | 8.0497          | 0.2508   | 0.2710                   | 0.2508                   | 0.2287                | 0.2508                | 0.2217            | 0.2508            |
| 0.0436        | 22.0  | 13970 | 8.3609          | 0.2469   | 0.2458                   | 0.2469                   | 0.2249                | 0.2469                | 0.2201            | 0.2469            |
| 0.0492        | 23.0  | 14605 | 8.4803          | 0.2547   | 0.2565                   | 0.2547                   | 0.2327                | 0.2547                | 0.2284            | 0.2547            |
| 0.03          | 24.0  | 15240 | 8.7246          | 0.2578   | 0.2793                   | 0.2578                   | 0.2353                | 0.2578                | 0.2318            | 0.2578            |
| 0.0322        | 25.0  | 15875 | 9.1533          | 0.2555   | 0.2607                   | 0.2555                   | 0.2277                | 0.2555                | 0.2192            | 0.2555            |
| 0.0468        | 26.0  | 16510 | 8.7821          | 0.2477   | 0.2717                   | 0.2477                   | 0.2242                | 0.2477                | 0.2202            | 0.2477            |
| 0.0327        | 27.0  | 17145 | 8.7916          | 0.2516   | 0.2609                   | 0.2516                   | 0.2307                | 0.2516                | 0.2296            | 0.2516            |
| 0.027         | 28.0  | 17780 | 9.0334          | 0.2477   | 0.2569                   | 0.2477                   | 0.2240                | 0.2477                | 0.2211            | 0.2477            |
| 0.0284        | 29.0  | 18415 | 9.1966          | 0.2492   | 0.2712                   | 0.2492                   | 0.2316                | 0.2492                | 0.2283            | 0.2492            |
| 0.0251        | 30.0  | 19050 | 9.3470          | 0.2594   | 0.2696                   | 0.2594                   | 0.2353                | 0.2594                | 0.2291            | 0.2594            |
| 0.0295        | 31.0  | 19685 | 9.5033          | 0.2562   | 0.2557                   | 0.2562                   | 0.2307                | 0.2562                | 0.2237            | 0.2562            |
| 0.0198        | 32.0  | 20320 | 9.5799          | 0.2609   | 0.2829                   | 0.2609                   | 0.2374                | 0.2609                | 0.2336            | 0.2609            |
| 0.0198        | 33.0  | 20955 | 9.4022          | 0.2562   | 0.2787                   | 0.2562                   | 0.2389                | 0.2562                | 0.2403            | 0.2562            |
| 0.0164        | 34.0  | 21590 | 9.8864          | 0.2547   | 0.2666                   | 0.2547                   | 0.2323                | 0.2547                | 0.2280            | 0.2547            |
| 0.0142        | 35.0  | 22225 | 9.6852          | 0.2602   | 0.2793                   | 0.2602                   | 0.2403                | 0.2602                | 0.2413            | 0.2602            |
| 0.011         | 36.0  | 22860 | 9.7627          | 0.2672   | 0.2866                   | 0.2672                   | 0.2431                | 0.2672                | 0.2412            | 0.2672            |
| 0.0126        | 37.0  | 23495 | 9.8309          | 0.2633   | 0.2717                   | 0.2633                   | 0.2415                | 0.2633                | 0.2395            | 0.2633            |
| 0.0102        | 38.0  | 24130 | 10.0910         | 0.2508   | 0.2561                   | 0.2508                   | 0.2257                | 0.2508                | 0.2196            | 0.2508            |
| 0.0117        | 39.0  | 24765 | 10.0140         | 0.2680   | 0.2749                   | 0.2680                   | 0.2455                | 0.2680                | 0.2417            | 0.2680            |
| 0.013         | 40.0  | 25400 | 9.9480          | 0.2687   | 0.2801                   | 0.2687                   | 0.2511                | 0.2687                | 0.2475            | 0.2687            |
| 0.0057        | 41.0  | 26035 | 10.0697         | 0.2602   | 0.2633                   | 0.2602                   | 0.2376                | 0.2602                | 0.2356            | 0.2602            |
| 0.0057        | 42.0  | 26670 | 10.0187         | 0.2555   | 0.2733                   | 0.2555                   | 0.2354                | 0.2555                | 0.2358            | 0.2555            |
| 0.0048        | 43.0  | 27305 | 10.1409         | 0.2578   | 0.2654                   | 0.2578                   | 0.2365                | 0.2578                | 0.2360            | 0.2578            |
| 0.0063        | 44.0  | 27940 | 10.1794         | 0.2578   | 0.2811                   | 0.2578                   | 0.2361                | 0.2578                | 0.2360            | 0.2578            |
| 0.0052        | 45.0  | 28575 | 10.3589         | 0.2508   | 0.2624                   | 0.2508                   | 0.2300                | 0.2508                | 0.2258            | 0.2508            |
| 0.0055        | 46.0  | 29210 | 10.2599         | 0.2672   | 0.2829                   | 0.2672                   | 0.2425                | 0.2672                | 0.2409            | 0.2672            |
| 0.0021        | 47.0  | 29845 | 10.3328         | 0.2656   | 0.2744                   | 0.2656                   | 0.2421                | 0.2656                | 0.2396            | 0.2656            |
| 0.0019        | 48.0  | 30480 | 10.3512         | 0.2586   | 0.2704                   | 0.2586                   | 0.2356                | 0.2586                | 0.2309            | 0.2586            |
| 0.004         | 49.0  | 31115 | 10.2833         | 0.2664   | 0.2725                   | 0.2664                   | 0.2432                | 0.2664                | 0.2409            | 0.2664            |
| 0.0014        | 50.0  | 31750 | 10.2776         | 0.2680   | 0.2765                   | 0.2680                   | 0.2444                | 0.2680                | 0.2423            | 0.2680            |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 1.18.4
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst-2-english-finetuned-Multi_classification", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-finetuned-sst-2-english-finetuned-Multi\_classification
===============================================================================


This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.6930
* Accuracy: 0.2680
* Macro Averaged Precision: 0.2166
* Micro Averaged Precision: 0.2680
* Macro Averaged Recall: 0.2327
* Micro Averaged Recall: 0.2680
* Macro Averaged F1: 0.2116
* Micro Averaged F1: 0.2680


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1+cu117
* Datasets 1.18.4
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1+cu117
* Datasets 1.18.4
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1+cu117
* Datasets 1.18.4
* Tokenizers 0.12.1