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# IndicBERTv2-MLM-Sam-TLM-NER

This model is a fine-tuned version of [ai4bharat/IndicBERTv2-MLM-Sam-TLM](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-Sam-TLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4521
- Precision: 0.7629
- Recall: 0.7792
- F1: 0.7710
- Accuracy: 0.9038

## 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: 128
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3268        | 0.49  | 1000  | 0.3440          | 0.7207    | 0.7602 | 0.7399 | 0.8887   |
| 0.2763        | 0.99  | 2000  | 0.3083          | 0.7568    | 0.7732 | 0.7649 | 0.8983   |
| 0.2604        | 1.48  | 3000  | 0.3312          | 0.7309    | 0.7494 | 0.7401 | 0.8909   |
| 0.2501        | 1.98  | 4000  | 0.3017          | 0.7415    | 0.7956 | 0.7676 | 0.9014   |
| 0.2269        | 2.47  | 5000  | 0.2930          | 0.7528    | 0.7970 | 0.7743 | 0.9050   |
| 0.223         | 2.96  | 6000  | 0.2963          | 0.7590    | 0.7963 | 0.7772 | 0.9053   |
| 0.2011        | 3.46  | 7000  | 0.2939          | 0.7627    | 0.7946 | 0.7783 | 0.9079   |
| 0.1999        | 3.95  | 8000  | 0.3036          | 0.7676    | 0.7903 | 0.7788 | 0.9069   |
| 0.1815        | 4.44  | 9000  | 0.3125          | 0.7618    | 0.7915 | 0.7764 | 0.9056   |
| 0.1777        | 4.94  | 10000 | 0.3083          | 0.7748    | 0.7957 | 0.7851 | 0.9098   |
| 0.1622        | 5.43  | 11000 | 0.3251          | 0.7721    | 0.7909 | 0.7814 | 0.9089   |
| 0.1598        | 5.93  | 12000 | 0.3197          | 0.7767    | 0.7947 | 0.7856 | 0.9092   |
| 0.145         | 6.42  | 13000 | 0.3366          | 0.7718    | 0.7986 | 0.7850 | 0.9101   |
| 0.1436        | 6.91  | 14000 | 0.3247          | 0.7776    | 0.7977 | 0.7875 | 0.9112   |
| 0.1306        | 7.41  | 15000 | 0.3502          | 0.7779    | 0.7958 | 0.7867 | 0.9107   |
| 0.1311        | 7.9   | 16000 | 0.3585          | 0.7857    | 0.7909 | 0.7883 | 0.9105   |
| 0.12          | 8.4   | 17000 | 0.3717          | 0.7768    | 0.7911 | 0.7839 | 0.9099   |
| 0.1202        | 8.89  | 18000 | 0.3667          | 0.7796    | 0.7882 | 0.7839 | 0.9100   |
| 0.1141        | 9.38  | 19000 | 0.3860          | 0.7857    | 0.7900 | 0.7879 | 0.9100   |
| 0.1113        | 9.88  | 20000 | 0.3824          | 0.7758    | 0.7970 | 0.7862 | 0.9094   |
| 0.1056        | 10.37 | 21000 | 0.4041          | 0.7740    | 0.7952 | 0.7845 | 0.9084   |
| 0.1073        | 10.86 | 22000 | 0.4062          | 0.7735    | 0.7929 | 0.7831 | 0.9094   |
| 0.1063        | 11.36 | 23000 | 0.4197          | 0.7720    | 0.7866 | 0.7793 | 0.9071   |
| 0.1026        | 11.85 | 24000 | 0.4179          | 0.7625    | 0.7767 | 0.7695 | 0.9040   |
| 0.1042        | 12.35 | 25000 | 0.4392          | 0.7639    | 0.7748 | 0.7693 | 0.9037   |
| 0.101         | 12.84 | 26000 | 0.4373          | 0.7533    | 0.7795 | 0.7662 | 0.9029   |
| 0.1003        | 13.33 | 27000 | 0.4554          | 0.7535    | 0.7774 | 0.7653 | 0.9021   |
| 0.0993        | 13.83 | 28000 | 0.4530          | 0.7555    | 0.7773 | 0.7663 | 0.9019   |
| 0.0978        | 14.32 | 29000 | 0.4467          | 0.7637    | 0.7843 | 0.7738 | 0.9050   |
| 0.0946        | 14.81 | 30000 | 0.4521          | 0.7629    | 0.7792 | 0.7710 | 0.9038   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "IndicBERTv2-MLM-Sam-TLM-NER", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

IndicBERTv2-MLM-Sam-TLM-NER
===========================


This model is a fine-tuned version of ai4bharat/IndicBERTv2-MLM-Sam-TLM on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4521
* Precision: 0.7629
* Recall: 0.7792
* F1: 0.7710
* Accuracy: 0.9038


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: 128
* eval\_batch\_size: 256
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 128
* eval\_batch\_size: 256
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 128
* eval\_batch\_size: 256
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3