model update

README.md

@@ -0,0 +1,126 @@
+---
+datasets:
+- wnut2017
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/deberta-v3-large-wnut2017
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wnut2017
+      type: wnut2017
+      args: wnut2017
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.5047353760445682
+    - name: Precision
+      type: precision
+      value: 0.63268156424581
+    - name: Recall
+      type: recall
+      value: 0.4198331788693234
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.4165125500830091
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.5356144444686111
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.3573954549633822
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.6249999999999999
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7962697274031564
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.5143651529193698
+
+pipeline_tag: token-classification
+widget:
+- text: "Jacob Collier is a Grammy awarded artist from England."
+  example_title: "NER Example 1"
+---
+# tner/deberta-v3-large-wnut2017
+
+This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the 
+[tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) 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): 0.5047353760445682
+- Precision (micro): 0.63268156424581
+- Recall (micro): 0.4198331788693234
+- F1 (macro): 0.4165125500830091
+- Precision (macro): 0.5356144444686111
+- Recall (macro): 0.3573954549633822
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.25477707006369427
+- group: 0.34309623430962344
+- location: 0.6187050359712232
+- person: 0.6721763085399448
+- product: 0.18579234972677597
+- work_of_art: 0.42452830188679247 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.4752384997212858, 0.5329114690850492]
+    - 95%: [0.46929053844001617, 0.537282841423422] 
+- F1 (macro): 
+    - 90%: [0.4752384997212858, 0.5329114690850492]
+    - 95%: [0.46929053844001617, 0.537282841423422] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/eval/metric_span.json).
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+ - dataset: ['tner/wnut2017']
+ - dataset_split: train
+ - dataset_name: None
+ - local_dataset: None
+ - model: microsoft/deberta-v3-large
+ - crf: False
+ - 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: 10.0
+
+The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-wnut2017/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/wnut2017_deberta_v3_large/best_model",
+  "_name_or_path": "tner_ckpt/wnut2017_deberta_large/model_ulfllg/epoch_5",
   "architectures": [
     "DebertaV2ForTokenClassification"
   ],

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cd25ecf2f9de84df2f277628834ec098f8598b173ed5b3f83ddc37e0ec69695c
-size 1736233903
+oid sha256:b8e660f4e6df99d5522db22b282d4ee5909a0486e908f1929f93312f00705206
+size 1736239407

tokenizer_config.json

@@ -4,7 +4,7 @@
   "do_lower_case": false,
   "eos_token": "[SEP]",
   "mask_token": "[MASK]",
-  "name_or_path": "tner_ckpt/wnut2017_deberta_v3_large/best_model",
+  "name_or_path": "tner_ckpt/wnut2017_deberta_large/model_ulfllg/epoch_5",
   "pad_token": "[PAD]",
   "sep_token": "[SEP]",
   "sp_model_kwargs": {},