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# roberta-large-md-conllpp

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conllpp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0457
- Precision: 0.9971
- Recall: 0.9968
- F1: 0.9970
- Accuracy: 0.9950

## 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: 1e-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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0748        | 1.0   | 878  | 0.0309          | 0.9959    | 0.9962 | 0.9961 | 0.9935   |
| 0.0111        | 2.0   | 1756 | 0.0346          | 0.9974    | 0.9967 | 0.9970 | 0.9951   |
| 0.0057        | 3.0   | 2634 | 0.0348          | 0.9974    | 0.9960 | 0.9967 | 0.9946   |
| 0.0031        | 4.0   | 3512 | 0.0434          | 0.9976    | 0.9964 | 0.9970 | 0.9951   |
| 0.0017        | 5.0   | 4390 | 0.0457          | 0.9971    | 0.9968 | 0.9970 | 0.9950   |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conllpp"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-large-md-conllpp", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conllpp", "type": "conllpp", "config": "conllpp", "split": "train", "args": "conllpp"}, "metrics": [{"type": "precision", "value": 0.9971177780689113, "name": "Precision"}, {"type": "recall", "value": 0.9968043586452576, "name": "Recall"}, {"type": "f1", "value": 0.9969610437242934, "name": "F1"}, {"type": "accuracy", "value": 0.995003768708948, "name": "Accuracy"}]}]}]}
transformers pytorch roberta token-classification generated_from_trainer dataset:conllpp license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conllpp #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

roberta-large-md-conllpp
========================


This model is a fine-tuned version of roberta-large on the conllpp dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0457
* Precision: 0.9971
* Recall: 0.9968
* F1: 0.9970
* Accuracy: 0.9950


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: 1e-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: 5


### Training results



### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 5 ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conllpp #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 5 ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conllpp #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 5### Training results### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1