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# T5-Encoder(T5-large model) fine-tuned on very small dataset for token classification

Simple experimental model that was trained in 3 epochs on very small dataset

## Usage

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification, NerPipeline

model = AutoModelForTokenClassification.from_pretrained("imvladikon/t5-english-ner", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("imvladikon/t5-english-ner", trust_remote_code=True)

pipe = NerPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="max")
print(pipe("London is the capital city of England and the United Kingdom"))
"""
[{'entity_group': 'LOCATION',
  'score': 0.84536326,
  'word': 'London',
  'start': 0,
  'end': 6},
 {'entity_group': 'LOCATION',
  'score': 0.8957489,
  'word': 'England',
  'start': 30,
  'end': 37},
 {'entity_group': 'LOCATION',
  'score': 0.73186326,
  'word': 'UnitedKingdom',
  'start': 46,
  'end': 60}]
"""
```

## Usage in spacy

```bash
pip install spacy transformers git+https://github.com/explosion/spacy-huggingface-pipelines -q
```

```python
import spacy
from spacy import displacy

text = "My name is Sarah and I live in London"

nlp = spacy.blank("en")
nlp.add_pipe("hf_token_pipe", config={"model": "imvladikon/t5-english-ner", "kwargs": {"trust_remote_code":True}})
doc = nlp(text)
print(doc.ents)
# (Sarah, London)
```


This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the private(en) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1956
- Commercial Item Precision: 0.0
- Commercial Item Recall: 0.0
- Commercial Item F1: 0.0
- Commercial Item Number: 1
- Date Precision: 0.8125
- Date Recall: 0.9286
- Date F1: 0.8667
- Date Number: 14
- Location Precision: 0.7143
- Location Recall: 0.75
- Location F1: 0.7317
- Location Number: 20
- Organization Precision: 0.8588
- Organization Recall: 0.9125
- Organization F1: 0.8848
- Organization Number: 80
- Other Precision: 0.3684
- Other Recall: 0.3333
- Other F1: 0.35
- Other Number: 21
- Person Precision: 0.8182
- Person Recall: 0.9310
- Person F1: 0.8710
- Person Number: 29
- Quantity Precision: 0.8
- Quantity Recall: 0.8571
- Quantity F1: 0.8276
- Quantity Number: 14
- Title Precision: 0.0
- Title Recall: 0.0
- Title F1: 0.0
- Title Number: 7
- Overall Precision: 0.75
- Overall Recall: 0.7903
- Overall F1: 0.7696
- Overall Accuracy: 0.9534

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Commercial Item Precision | Commercial Item Recall | Commercial Item F1 | Commercial Item Number | Date Precision | Date Recall | Date F1 | Date Number | Location Precision | Location Recall | Location F1 | Location Number | Organization Precision | Organization Recall | Organization F1 | Organization Number | Other Precision | Other Recall | Other F1 | Other Number | Person Precision | Person Recall | Person F1 | Person Number | Quantity Precision | Quantity Recall | Quantity F1 | Quantity Number | Title Precision | Title Recall | Title F1 | Title Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------:|:----------------------:|:------------------:|:----------------------:|:--------------:|:-----------:|:-------:|:-----------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------:|:------------:|:--------:|:------------:|:----------------:|:-------------:|:---------:|:-------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.8868        | 1.0   | 708  | 0.2725          | 0.0                       | 0.0                    | 0.0                | 1                      | 0.8125         | 0.9286      | 0.8667  | 14          | 0.4167             | 0.75            | 0.5357      | 20              | 0.8272                 | 0.8375              | 0.8323          | 80                  | 1.0             | 0.0476       | 0.0909   | 21           | 0.8438           | 0.9310        | 0.8852    | 29            | 0.6667             | 0.7143          | 0.6897      | 14              | 0.0             | 0.0          | 0.0      | 7            | 0.7348            | 0.7151         | 0.7248     | 0.9446           |
| 0.2984        | 2.0   | 1416 | 0.2121          | 0.0                       | 0.0                    | 0.0                | 1                      | 0.8667         | 0.9286      | 0.8966  | 14          | 0.5                | 0.8             | 0.6154      | 20              | 0.8375                 | 0.8375              | 0.8375          | 80                  | 0.3077          | 0.1905       | 0.2353   | 21           | 0.8182           | 0.9310        | 0.8710    | 29            | 0.7333             | 0.7857          | 0.7586      | 14              | 0.0             | 0.0          | 0.0      | 7            | 0.7077            | 0.7419         | 0.7244     | 0.9481           |
| 0.1729        | 3.0   | 2124 | 0.1956          | 0.0                       | 0.0                    | 0.0                | 1                      | 0.8125         | 0.9286      | 0.8667  | 14          | 0.7143             | 0.75            | 0.7317      | 20              | 0.8588                 | 0.9125              | 0.8848          | 80                  | 0.3684          | 0.3333       | 0.35     | 21           | 0.8182           | 0.9310        | 0.8710    | 29            | 0.8                | 0.8571          | 0.8276      | 14              | 0.0             | 0.0          | 0.0      | 7            | 0.75              | 0.7903         | 0.7696     | 0.9534           |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
## WANDB
[training logs and reports](https://wandb.ai/imvladikon/huggingface/runs/uyl6ihl1)
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["private"], "base_model": "t5-large", "model-index": [{"name": "ner-news-t5-large", "results": []}]}
transformers pytorch t5 token-classification generated_from_trainer custom_code dataset:private base_model:t5-large license:apache-2.0 autotrain_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #token-classification #generated_from_trainer #custom_code #dataset-private #base_model-t5-large #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us 

T5-Encoder(T5-large model) fine-tuned on very small dataset for token classification
====================================================================================


Simple experimental model that was trained in 3 epochs on very small dataset


Usage
-----


Usage in spacy
--------------


This model is a fine-tuned version of t5-large on the private(en) dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1956
* Commercial Item Precision: 0.0
* Commercial Item Recall: 0.0
* Commercial Item F1: 0.0
* Commercial Item Number: 1
* Date Precision: 0.8125
* Date Recall: 0.9286
* Date F1: 0.8667
* Date Number: 14
* Location Precision: 0.7143
* Location Recall: 0.75
* Location F1: 0.7317
* Location Number: 20
* Organization Precision: 0.8588
* Organization Recall: 0.9125
* Organization F1: 0.8848
* Organization Number: 80
* Other Precision: 0.3684
* Other Recall: 0.3333
* Other F1: 0.35
* Other Number: 21
* Person Precision: 0.8182
* Person Recall: 0.9310
* Person F1: 0.8710
* Person Number: 29
* Quantity Precision: 0.8
* Quantity Recall: 0.8571
* Quantity F1: 0.8276
* Quantity Number: 14
* Title Precision: 0.0
* Title Recall: 0.0
* Title F1: 0.0
* Title Number: 7
* Overall Precision: 0.75
* Overall Recall: 0.7903
* Overall F1: 0.7696
* Overall Accuracy: 0.9534


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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1


WANDB
-----


training logs and reports



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1


WANDB
-----


training logs and reports
TAGS
#transformers #pytorch #t5 #token-classification #generated_from_trainer #custom_code #dataset-private #base_model-t5-large #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1


WANDB
-----


training logs and reports
TAGS
#transformers #pytorch #t5 #token-classification #generated_from_trainer #custom_code #dataset-private #base_model-t5-large #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1


WANDB
-----


training logs and reports