README.md

---
datasets:
- tner/mit_movie_trivia
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-mit-movie-trivia
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: tner/mit_movie_trivia
      type: tner/mit_movie_trivia
      args: tner/mit_movie_trivia
    metrics:
    - name: F1
      type: f1
      value: 0.7324478178368122
    - name: Precision
      type: precision
      value: 0.7186865267433988
    - name: Recall
      type: recall
      value: 0.746746394653535
    - name: F1 (macro)
      type: f1_macro
      value: 0.6597589403836301
    - name: Precision (macro)
      type: precision_macro
      value: 0.6493939604029393
    - name: Recall (macro)
      type: recall_macro
      value: 0.6747458149186768
    - name: F1 (entity span)
      type: f1_entity_span
      value: 0.749525289142068
    - name: Precision (entity span)
      type: precision_entity_span
      value: 0.7359322033898306
    - name: Recall (entity span)
      type: recall_entity_span
      value: 0.7636299683432993

pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
  example_title: "NER Example 1"
---
# tner/deberta-v3-large-mit-movie-trivia

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the 
[tner/mit_movie_trivia](https://huggingface.co/datasets/tner/mit_movie_trivia) dataset.
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:
- F1 (micro): 0.7324478178368122
- Precision (micro): 0.7186865267433988
- Recall (micro): 0.746746394653535
- F1 (macro): 0.6597589403836301
- Precision (macro): 0.6493939604029393
- Recall (macro): 0.6747458149186768

The per-entity breakdown of the F1 score on the test set are below:
- actor: 0.9590417310664605
- award: 0.4755244755244755
- character_name: 0.7391304347826086
- date: 0.9640179910044978
- director: 0.909706546275395
- genre: 0.755114693118413
- opinion: 0.4910714285714286
- origin: 0.3922518159806296
- plot: 0.4929757343550447
- quote: 0.7391304347826088
- relationship: 0.5705705705705706
- soundtrack: 0.42857142857142855 

For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro): 
    - 90%: [0.7213456287685677, 0.742502895519075]
    - 95%: [0.7198169787204788, 0.7460320515170399] 
- F1 (macro): 
    - 90%: [0.7213456287685677, 0.742502895519075]
    - 95%: [0.7198169787204788, 0.7460320515170399] 

Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-mit-movie-trivia/raw/main/eval/metric.json) 
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-mit-movie-trivia/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/deberta-v3-large-mit-movie-trivia")
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/mit_movie_trivia']
 - dataset_split: train
 - dataset_name: None
 - local_dataset: None
 - model: microsoft/deberta-v3-large
 - crf: True
 - max_length: 128
 - epoch: 15
 - batch_size: 16
 - lr: 1e-05
 - random_seed: 42
 - gradient_accumulation_steps: 4
 - weight_decay: 1e-07
 - lr_warmup_step_ratio: 0.1
 - max_grad_norm: None

The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-mit-movie-trivia/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.",
}

```