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# saved_model_git-base

This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2473
- Wer Score: 2.7325
- Rouge1: 0.3059
- Rouge2: 0.1738
- Rougel: 0.2760
- Rougelsum: 0.2759
- Meteor: 0.4991
- Bleu: 0.1058
- Bleu1: 0.2113
- Bleu2: 0.1272
- Bleu3: 0.0824
- Bleu4: 0.0566

## 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: 5e-05
- train_batch_size: 112
- eval_batch_size: 112
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 224
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer Score | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor | Bleu   | Bleu1  | Bleu2  | Bleu3  | Bleu4  |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:------:|:---------:|:------:|:------:|:------:|:------:|:------:|:------:|
| 0.774         | 1.7   | 1000  | 0.2771          | 3.5978    | 0.2206 | 0.1145 | 0.1981 | 0.1981    | 0.4163 | 0.0774 | 0.1712 | 0.0965 | 0.0580 | 0.0375 |
| 0.2763        | 3.4   | 2000  | 0.2537          | 3.6165    | 0.2273 | 0.1237 | 0.2050 | 0.2050    | 0.4374 | 0.0840 | 0.1757 | 0.1032 | 0.0642 | 0.0428 |
| 0.2567        | 5.11  | 3000  | 0.2423          | 3.5963    | 0.2317 | 0.1299 | 0.2105 | 0.2105    | 0.4500 | 0.0881 | 0.1790 | 0.1074 | 0.0681 | 0.0460 |
| 0.2447        | 6.81  | 4000  | 0.2349          | 3.5915    | 0.2352 | 0.1336 | 0.2136 | 0.2136    | 0.4573 | 0.0907 | 0.1812 | 0.1100 | 0.0706 | 0.0481 |
| 0.2357        | 8.51  | 5000  | 0.2297          | 3.5867    | 0.2364 | 0.1364 | 0.2158 | 0.2158    | 0.4617 | 0.0927 | 0.1820 | 0.1120 | 0.0726 | 0.0499 |
| 0.2287        | 10.21 | 6000  | 0.2258          | 3.5781    | 0.2393 | 0.1392 | 0.2183 | 0.2183    | 0.4681 | 0.0947 | 0.1837 | 0.1139 | 0.0745 | 0.0515 |
| 0.2228        | 11.91 | 7000  | 0.2223          | 3.5628    | 0.2413 | 0.1419 | 0.2208 | 0.2208    | 0.4734 | 0.0965 | 0.1853 | 0.1158 | 0.0762 | 0.0531 |
| 0.2173        | 13.62 | 8000  | 0.2200          | 3.5171    | 0.2459 | 0.1452 | 0.2249 | 0.2249    | 0.4779 | 0.0976 | 0.1860 | 0.1167 | 0.0773 | 0.0540 |
| 0.2132        | 15.32 | 9000  | 0.2184          | 3.5207    | 0.2461 | 0.1464 | 0.2253 | 0.2254    | 0.4804 | 0.0994 | 0.1885 | 0.1187 | 0.0789 | 0.0553 |
| 0.2085        | 17.02 | 10000 | 0.2174          | 3.5189    | 0.2484 | 0.1468 | 0.2259 | 0.2259    | 0.4842 | 0.0998 | 0.1895 | 0.1190 | 0.0791 | 0.0555 |
| 0.2027        | 18.72 | 11000 | 0.2179          | 3.2891    | 0.2656 | 0.1571 | 0.2411 | 0.2411    | 0.4952 | 0.1036 | 0.1970 | 0.1233 | 0.0820 | 0.0577 |
| 0.1961        | 20.43 | 12000 | 0.2213          | 3.3457    | 0.2610 | 0.1534 | 0.2367 | 0.2367    | 0.4900 | 0.1025 | 0.1962 | 0.1223 | 0.0810 | 0.0568 |
| 0.1886        | 22.13 | 13000 | 0.2260          | 2.9878    | 0.2914 | 0.1696 | 0.2628 | 0.2628    | 0.5028 | 0.1053 | 0.2040 | 0.1257 | 0.0828 | 0.0579 |
| 0.1797        | 23.83 | 14000 | 0.2305          | 3.0250    | 0.2874 | 0.1668 | 0.2597 | 0.2597    | 0.4987 | 0.1053 | 0.2051 | 0.1259 | 0.0827 | 0.0575 |
| 0.1713        | 25.53 | 15000 | 0.2376          | 2.7048    | 0.3125 | 0.1797 | 0.2822 | 0.2822    | 0.5062 | 0.1078 | 0.2125 | 0.1291 | 0.0843 | 0.0583 |
| 0.1646        | 27.23 | 16000 | 0.2438          | 2.7129    | 0.3087 | 0.1761 | 0.2786 | 0.2785    | 0.5021 | 0.1066 | 0.2120 | 0.1281 | 0.0831 | 0.0573 |
| 0.159         | 28.94 | 17000 | 0.2473          | 2.7325    | 0.3059 | 0.1738 | 0.2760 | 0.2759    | 0.4991 | 0.1058 | 0.2113 | 0.1272 | 0.0824 | 0.0566 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["rouge", "bleu"], "model-index": [{"name": "saved_model_git-base", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "rouge", "value": 0.3058988098589094, "name": "Rouge1"}, {"type": "bleu", "value": 0.10580263597345552, "name": "Bleu"}]}]}]}
transformers pytorch tensorboard git text-generation generated_from_trainer dataset:imagefolder license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #git #text-generation #generated_from_trainer #dataset-imagefolder #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

saved\_model\_git-base
======================


This model is a fine-tuned version of microsoft/git-base on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2473
* Wer Score: 2.7325
* Rouge1: 0.3059
* Rouge2: 0.1738
* Rougel: 0.2760
* Rougelsum: 0.2759
* Meteor: 0.4991
* Bleu: 0.1058
* Bleu1: 0.2113
* Bleu2: 0.1272
* Bleu3: 0.0824
* Bleu4: 0.0566


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: 5e-05
* train\_batch\_size: 112
* eval\_batch\_size: 112
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 224
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 112
* eval\_batch\_size: 112
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 224
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #git #text-generation #generated_from_trainer #dataset-imagefolder #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 112
* eval\_batch\_size: 112
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 224
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3