
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# twitter_ner

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0142
- Precision: 0.9328
- Recall: 0.9504
- F1: 0.9415
- Accuracy: 0.9978

## 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: 8e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 30   | 0.2020          | 0.2932    | 0.0479 | 0.0824 | 0.9516   |
| No log        | 2.0   | 60   | 0.1365          | 0.4653    | 0.3328 | 0.3880 | 0.9651   |
| No log        | 3.0   | 90   | 0.0783          | 0.6292    | 0.6125 | 0.6207 | 0.9803   |
| No log        | 4.0   | 120  | 0.0465          | 0.7196    | 0.7793 | 0.7483 | 0.9877   |
| No log        | 5.0   | 150  | 0.0285          | 0.8493    | 0.8725 | 0.8608 | 0.9936   |
| No log        | 6.0   | 180  | 0.0197          | 0.9012    | 0.9204 | 0.9107 | 0.9963   |
| No log        | 7.0   | 210  | 0.0157          | 0.9124    | 0.9350 | 0.9235 | 0.9969   |
| No log        | 8.0   | 240  | 0.0142          | 0.9328    | 0.9504 | 0.9415 | 0.9978   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

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

twitter\_ner
============


This model is a fine-tuned version of distilbert-base-cased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0142
* Precision: 0.9328
* Recall: 0.9504
* F1: 0.9415
* Accuracy: 0.9978


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: 8e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8


### Training results



### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 8e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #distilbert #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: 8e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3