

# akhisreelibra/bert-malayalam-pos-tagger

This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on a set of tagged Malayalam sentence dataset
It achieves the following results on the evaluation set:
- Loss: 0.4383
- Precision: 0.7380
- Recall: 0.7767
- F1: 0.7569
- Accuracy: 0.8552

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

| Tag abbreviation  | Tag name                 | 
|:-----------------:|:------------------------:|
| 'CC_CCD'          |  Co-ordinator            |
| 'CC_CCS'          |  Subordinator            |
| 'CC_CCS_UT'       |  Quotative               |
| 'DM_DMD'          |  Deictic demonstrative   |
| 'DM_DMQ'          |  Wh-word                 |
| 'DM_DMR'          |  Relative demonstrative  |
| 'JJ'              |  Adjective               |
| 'N_NN'            |  Common noun             |
| 'N_NNP'           |  Proper noun             |
| 'N_NST'           |  Locative noun           |
| 'PR_PRC'          |  Reciprocal pronoun      |
| 'PR_PRF'          |  Reflexive pronoun       |
| 'PR_PRL'          |  Relative pronoun        |
| 'PR_PRP'          |  Personal pronoun        |
| 'PR_PRQ'          |  Wh-word                 |
| 'PSP'             |  Postposition            |
| 'QT_QTC'          |  Cardinals               |
| 'QT_QTF'          |  General quantifier      |
| 'QT_QTO'          |  Ordinals                |
| 'RB'              |  Adverb                  |
| 'RD_ECH'          |  Echo words              |
| 'RD_RDF'          |  Foreign words           |
| 'RD_SYM'          |  Symbol                  |
| 'RD_UNK'          |  Unknown                 |
| 'RP_CL'           |  Classifier particle     |
| 'RP_INJ'          |  Interjection particle   |
| 'RP_INTF'         |  Intensifier particle    |
| 'RP_NEG'          |  Negation particle       |
| 'RP_RPD'          |  Default particle        |
| 'V_VAUX'          |  Auxiliary verb          |
| 'V_VM'            |  Main verb               |
| 'V_VM_VF'         |  Finite verb             |
| 'V_VM_VINF'       |  Infinite verb           |
| 'V_VM_VNF'        |  Non-finite verb         |
| 'V_VN'            |  Verbal noun             |
## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.518         | 1.0   | 2692 | 0.4987          | 0.7124    | 0.7415 | 0.7267 | 0.8374   |
| 0.4415        | 2.0   | 5384 | 0.4515          | 0.7221    | 0.7679 | 0.7443 | 0.8481   |
| 0.3645        | 3.0   | 8076 | 0.4383          | 0.7380    | 0.7767 | 0.7569 | 0.8552   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-malayalam-pos-tagger", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

akhisreelibra/bert-malayalam-pos-tagger
=======================================


This model is a fine-tuned version of bert-base-multilingual-uncased on a set of tagged Malayalam sentence dataset
It achieves the following results on the evaluation set:


* Loss: 0.4383
* Precision: 0.7380
* Recall: 0.7767
* F1: 0.7569
* Accuracy: 0.8552


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


More information needed


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


More information needed


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



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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1