model update

README.md

@@ -0,0 +1,136 @@
+---
+datasets:
+- fin
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/deberta-v3-large-fin
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: fin
+      type: fin
+      args: fin
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.6430868167202574
+    - name: Precision
+      type: precision
+      value: 0.6578947368421053
+    - name: Recall
+      type: recall
+      value: 0.6289308176100629
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.37234464254803534
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.3758815642868512
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.3836106023606024
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.6883116883116883
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7043189368770764
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.6730158730158731
+
+pipeline_tag: token-classification
+widget:
+- text: "Jacob Collier is a Grammy awarded artist from England."
+  example_title: "NER Example 1"
+---
+# tner/deberta-v3-large-fin
+
+This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the 
+[tner/fin](https://huggingface.co/datasets/tner/fin) 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): 0.6430868167202574
+- Precision (micro): 0.6578947368421053
+- Recall (micro): 0.6289308176100629
+- F1 (macro): 0.37234464254803534
+- Precision (macro): 0.3758815642868512
+- Recall (macro): 0.3836106023606024
+
+The per-entity breakdown of the F1 score on the test set are below:
+- LOC: nan
+- MISC: nan
+- ORG: nan
+- PER: nan 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.5722111059165758, 0.7112704135498799]
+    - 95%: [0.557944362785127, 0.725353903079494] 
+- F1 (macro): 
+    - 90%: [0.5722111059165758, 0.7112704135498799]
+    - 95%: [0.557944362785127, 0.725353903079494] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-fin/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-fin/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-fin")
+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/fin']
+ - dataset_split: train
+ - dataset_name: None
+ - local_dataset: None
+ - model: microsoft/deberta-v3-large
+ - crf: False
+ - max_length: 128
+ - epoch: 17
+ - 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: 10.0
+
+The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-fin/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.",
+}
+
+```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "tner_ckpt/fin_deberta_v3_large/best_model",
+  "_name_or_path": "tner_ckpt/fin_deberta_v3_large/model_ulfllg/epoch_16",
   "architectures": [
     "DebertaV2ForTokenClassification"
   ],

eval/metric.json

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+{"micro/f1": 0.6430868167202574, "micro/f1_ci": {"90": [0.5722111059165758, 0.7112704135498799], "95": [0.557944362785127, 0.725353903079494]}, "micro/recall": 0.6289308176100629, "micro/precision": 0.6578947368421053, "macro/f1": 0.37234464254803534, "macro/f1_ci": {"90": [0.321037444212583, 0.4174222520031422], "95": [0.3126661472561014, 0.4276527473028317]}, "macro/recall": 0.3836106023606024, "macro/precision": 0.3758815642868512, "per_entity_metric": {"LOC": {"f1": NaN, "f1_ci": {"90": [NaN, NaN], "95": [NaN, NaN]}, "precision": 0.0, "recall": 0.0}, "MISC": {"f1": NaN, "f1_ci": {"90": [NaN, NaN], "95": [NaN, NaN]}, "precision": 0.0, "recall": 0.0}, "ORG": {"f1": NaN, "f1_ci": {"90": [NaN, NaN], "95": [NaN, NaN]}, "precision": 0.0, "recall": 0.0}, "PER": {"f1": NaN, "f1_ci": {"90": [NaN, NaN], "95": [NaN, NaN]}, "precision": 0.0, "recall": 0.0}}}

eval/metric_span.json

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+{"micro/f1": 0.6883116883116883, "micro/f1_ci": {"90": [0.6137984272716044, 0.757765305655086], "95": [0.604156373368873, 0.7718631178707224]}, "micro/recall": 0.6730158730158731, "micro/precision": 0.7043189368770764, "macro/f1": 0.6883116883116883, "macro/f1_ci": {"90": [0.6137984272716044, 0.757765305655086], "95": [0.604156373368873, 0.7718631178707224]}, "macro/recall": 0.6730158730158731, "macro/precision": 0.7043189368770764}

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:011ac87e23c7729d64bcba4abeb513607010d1f887bac0ae014e29bd6374edf7
-size 1736217519
+oid sha256:b043f9a9734e2e12de53c5dbdeb48ff85def0ef8c78bea2f8bc73eabbcbd2198
+size 1736223023

tokenizer_config.json

@@ -4,7 +4,7 @@
   "do_lower_case": false,
   "eos_token": "[SEP]",
   "mask_token": "[MASK]",
-  "name_or_path": "tner_ckpt/fin_deberta_v3_large/best_model",
+  "name_or_path": "tner_ckpt/fin_deberta_v3_large/model_ulfllg/epoch_16",
   "pad_token": "[PAD]",
   "sep_token": "[SEP]",
   "sp_model_kwargs": {},

trainer_config.json

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+{"dataset": ["tner/fin"], "dataset_split": "train", "dataset_name": null, "local_dataset": null, "model": "microsoft/deberta-v3-large", "crf": false, "max_length": 128, "epoch": 17, "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": 10.0}