
<!-- 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. -->

# distilbert-base-uncased-finetuned-ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0614
- Precision: 0.9228
- Recall: 0.9360
- F1: 0.9294
- Accuracy: 0.9833

## 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: 2e-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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2433        | 1.0   | 878  | 0.0732          | 0.9079    | 0.9190 | 0.9134 | 0.9795   |
| 0.0553        | 2.0   | 1756 | 0.0599          | 0.9170    | 0.9333 | 0.9251 | 0.9826   |
| 0.0305        | 3.0   | 2634 | 0.0614          | 0.9228    | 0.9360 | 0.9294 | 0.9833   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9227969559942649, "name": "Precision"}, {"type": "recall", "value": 0.9360107394563151, "name": "Recall"}, {"type": "f1", "value": 0.9293568810396535, "name": "F1"}, {"type": "accuracy", "value": 0.9833034139831922, "name": "Accuracy"}]}, {"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "config": "conll2003", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.973914094330502, "name": "Accuracy", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmJmZTE4OGY4MmNlZGJmMzJmZGYxMjQ5Nzc4MzEzODU2YjQwZWQ1ZDU1N2NmN2M2YjliZTQ3MmZhZjA2OGYwNCIsInZlcnNpb24iOjF9.w_Y03WPSKDkQnyC3FFw4qtffWqg4ZbjJ6zyIEl6dKTCf6rgrjbhJKIb3MsOIw34Ydb-M3TTpV2Ak43bsaXQ-DA"}, {"type": "precision", "value": 0.9791360147483736, "name": "Precision", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODY2YjUwYTk5NGE1YWJlYjMyM2MyZGU4ZjE2MTM1ZGZiZDg4MTFjMGRkNzI5ODQ0ZTBlMmVkYzkyODIwYjgxMCIsInZlcnNpb24iOjF9.nChULEs9H0UFNtlM4m_kuBm9Ch981r7V4Axo1yvPIoPAPd6GyCopO615pyjd7bwXxYy4_nQpc1cBI5iY0OkHDA"}, {"type": "recall", "value": 0.9793269742207723, "name": "Recall", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmI0MzRkZjY4M2Y5YWE1OTdjNDNlN2NmNDVhMmEwODI2MmM1ZTViNDc1NzllZDdkOWZiZWVkMjQxNGM0YTQyZCIsInZlcnNpb24iOjF9.jS1iBDeJK7_QB7kanNxyfAnZm0HdS_EqBPjBCVhYCPEMRLnuXeuztdz_G4MczcZV6F2RoDjLJzxJdbuzKN1eCw"}, {"type": "f1", "value": 0.9792314851748437, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmQ1MjM1ZDU2YzlmY2JkYTU0MjU5MTIzNDc3MDZmNzJjZmNkNzI1ZDY0MWFmYjBhZjI5NTg3ZjY0NGFlYWZmOSIsInZlcnNpb24iOjF9.BtgL5tCizs8iH7LHOfl1aRfaW0Nxfx6kWldUmWbjDk_McZrK6BRxFnHDscVZ1wUa11rX1IjgC1_DOcMNBXq6BQ"}, {"type": "loss", "value": 0.10710480064153671, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWU0MDY3OTAxZTUyNmNlMjA1MDdiNTg4ZmI4MTJmMDYyMTY4MjZjYzNkODFlMDY1M2RjMjMyNDkzNzBkMmQzNiIsInZlcnNpb24iOjF9.dU5jfYPYWXkiebzZ_c4HTxui6RoYYfAdShcSzXBY0v-pB9FEwm_-8vHOtT-rK_s_EwifpPobRfdpXL2Y7C33CA"}]}]}]}
transformers pytorch tensorboard safetensors distilbert token-classification generated_from_trainer dataset:conll2003 base_model:distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-finetuned-ner
=====================================


This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0614
* Precision: 0.9228
* Recall: 0.9360
* F1: 0.9294
* Accuracy: 0.9833


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: 2e-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: 3


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 3 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 3 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 3### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6