README.md

---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-base-tweetner7-all
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: tner/tweetner7
      type: tner/tweetner7
      args: tner/tweetner7
    metrics:
    - name: F1 (test_2021)
      type: f1
      value: 0.6515831894070236
    - name: Precision (test_2021)
      type: precision
      value: 0.6488190781930749
    - name: Recall (test_2021)
      type: recall
      value: 0.6543709528214616
    - name: Macro F1 (test_2021)
      type: f1_macro
      value: 0.6081318073591985
    - name: Macro Precision (test_2021)
      type: precision_macro
      value: 0.6024892144112918
    - name: Macro Recall (test_2021)
      type: recall_macro
      value: 0.6155807376978756
    - name: Entity Span F1 (test_2021)
      type: f1_entity_span
      value: 0.7893373251194657
    - name: Entity Span Precision (test_2020)
      type: precision_entity_span
      value: 0.7859435909195138
    - name: Entity Span Recall (test_2021)
      type: recall_entity_span
      value: 0.7927604949693535
    - name: F1 (test_2020)
      type: f1
      value: 0.6531839300355288
    - name: Precision (test_2020)
      type: precision
      value: 0.6899538106235565
    - name: Recall (test_2020)
      type: recall
      value: 0.6201349247535028
    - name: Macro F1 (test_2020)
      type: f1_macro
      value: 0.6166186507300974
    - name: Macro Precision (test_2020)
      type: precision_macro
      value: 0.6523781324413148
    - name: Macro Recall (test_2020)
      type: recall_macro
      value: 0.5860926262979317
    - name: Entity Span F1 (test_2020)
      type: f1_entity_span
      value: 0.7523236741388737
    - name: Entity Span Precision (test_2020)
      type: precision_entity_span
      value: 0.7949162333911034
    - name: Entity Span Recall (test_2020)
      type: recall_entity_span
      value: 0.7140633108458744

pipeline_tag: token-classification
widget:
- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {{@Herbie Hancock@}} via {{USERNAME}} link below: {{URL}}"
  example_title: "NER Example 1"
---
# tner/roberta-base-tweetner7-all

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the 
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_all` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.6515831894070236
- Precision (micro): 0.6488190781930749
- Recall (micro): 0.6543709528214616
- F1 (macro): 0.6081318073591985
- Precision (macro): 0.6024892144112918
- Recall (macro): 0.6155807376978756



The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.5174234424498415
- creative_work: 0.466403162055336
- event: 0.46727272727272723
- group: 0.6071197411003236
- location: 0.6832786885245901
- person: 0.8377301195672804
- product: 0.6776947705442904 

For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro): 
    - 90%: [0.6426248846161623, 0.6611146727643068]
    - 95%: [0.6408583849998567, 0.6629609445072536] 
- F1 (macro): 
    - 90%: [0.6426248846161623, 0.6611146727643068]
    - 95%: [0.6408583849998567, 0.6629609445072536] 

Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-base-tweetner7-all/raw/main/eval/metric.json) 
and [metric file of entity span](https://huggingface.co/tner/roberta-base-tweetner7-all/raw/main/eval/metric_span.json).

### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip   
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/roberta-base-tweetner7-all")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.

### Training hyperparameters

The following hyperparameters were used during training:
 - dataset: ['tner/tweetner7']
 - dataset_split: train_all
 - dataset_name: None
 - local_dataset: None
 - model: roberta-base
 - crf: True
 - max_length: 128
 - epoch: 30
 - batch_size: 32
 - lr: 1e-05
 - random_seed: 0
 - gradient_accumulation_steps: 1
 - weight_decay: 1e-07
 - lr_warmup_step_ratio: 0.3
 - max_grad_norm: 1

The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-base-tweetner7-all/raw/main/trainer_config.json).

### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).

```

@inproceedings{ushio-camacho-collados-2021-ner,
    title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
    author = "Ushio, Asahi  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eacl-demos.7",
    doi = "10.18653/v1/2021.eacl-demos.7",
    pages = "53--62",
    abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}

```