model update

README.md

@@ -0,0 +1,124 @@
+---
+datasets:
+- conll2003
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/deberta-v3-large-conll2003
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      args: conll2003
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.9222388190844389
+    - name: Precision
+      type: precision
+      value: 0.9154020582592011
+    - name: Recall
+      type: recall
+      value: 0.9291784702549575
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.9043961692086329
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.8959854326377331
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.9135442454672595
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.960570322126386
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.9550227511375569
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.9661827195467422
+
+pipeline_tag: token-classification
+widget:
+- text: "Jacob Collier is a Grammy awarded artist from England."
+  example_title: "NER Example 1"
+---
+# tner/deberta-v3-large-conll2003
+
+This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the 
+[tner/conll2003](https://huggingface.co/datasets/tner/conll2003) 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): 0.9222388190844389
+- Precision (micro): 0.9154020582592011
+- Recall (micro): 0.9291784702549575
+- F1 (macro): 0.9043961692086329
+- Precision (macro): 0.8959854326377331
+- Recall (macro): 0.9135442454672595
+
+The per-entity breakdown of the F1 score on the test set are below:
+- location: 0.9407496977025392
+- organization: 0.9115486335586247
+- other: 0.7920110192837466
+- person: 0.9732753262896209 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.9157944386463721, 0.9286928993636353]
+    - 95%: [0.9146558483630953, 0.9297919809412201] 
+- F1 (macro): 
+    - 90%: [0.9157944386463721, 0.9286928993636353]
+    - 95%: [0.9146558483630953, 0.9297919809412201] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-conll2003/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-conll2003/raw/main/eval/metric_span.json).
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+ - dataset: ['tner/conll2003']
+ - 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: None
+ - 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-conll2003/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/conll2003_deberta_v3_large/best_model",
+  "_name_or_path": "microsoft/deberta-v3-large",
   "architectures": [
     "DebertaV2ForTokenClassification"
   ],
@@ -51,7 +51,7 @@
   "relative_attention": true,
   "share_att_key": true,
   "torch_dtype": "float32",
-  "transformers_version": "4.20.1",
+  "transformers_version": "4.21.1",
   "type_vocab_size": 0,
   "vocab_size": 128100
 }

eval/metric.json

@@ -0,0 +1 @@
+{"micro/f1": 0.9222388190844389, "micro/f1_ci": {"90": [0.9157944386463721, 0.9286928993636353], "95": [0.9146558483630953, 0.9297919809412201]}, "micro/recall": 0.9291784702549575, "micro/precision": 0.9154020582592011, "macro/f1": 0.9043961692086329, "macro/f1_ci": {"90": [0.8967634601572929, 0.9120550234663798], "95": [0.8951146833014633, 0.9135073150082113]}, "macro/recall": 0.9135442454672595, "macro/precision": 0.8959854326377331, "per_entity_metric": {"location": {"f1": 0.9407496977025392, "f1_ci": {"90": [0.9327218512678329, 0.9484554935162461], "95": [0.9306220735040278, 0.9500172057211348]}, "precision": 0.948780487804878, "recall": 0.9328537170263789}, "organization": {"f1": 0.9115486335586247, "f1_ci": {"90": [0.9022883361987417, 0.9209639524074142], "95": [0.900931069471536, 0.9230338443865153]}, "precision": 0.8903559127439724, "recall": 0.9337748344370861}, "other": {"f1": 0.7920110192837466, "f1_ci": {"90": [0.7703470703328422, 0.8155099589242811], "95": [0.7655908711257591, 0.819674907814652]}, "precision": 0.7666666666666667, "recall": 0.8190883190883191}, "person": {"f1": 0.9732753262896209, "f1_ci": {"90": [0.9666809784122652, 0.9793078490377144], "95": [0.9648667613328867, 0.9802750787465647]}, "precision": 0.9781386633354153, "recall": 0.9684601113172542}}}

eval/metric_span.json

@@ -0,0 +1 @@
+{"micro/f1": 0.960570322126386, "micro/f1_ci": {"90": [0.9562602448386762, 0.9649430028068969], "95": [0.9549445046545261, 0.965818354349358]}, "micro/recall": 0.9661827195467422, "micro/precision": 0.9550227511375569, "macro/f1": 0.960570322126386, "macro/f1_ci": {"90": [0.9562602448386762, 0.9649430028068969], "95": [0.9549445046545261, 0.965818354349358]}, "macro/recall": 0.9661827195467422, "macro/precision": 0.9550227511375569}

pytorch_model.bin

@@ -1,3 +1,3 @@
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-size 1736217519
+oid sha256:72241d27925a8a7fbdfa8f15e0257b46d094c2122ba5417741dbd38cf7ffb48c
+size 1736223023

tokenizer_config.json

@@ -4,7 +4,7 @@
   "do_lower_case": false,
   "eos_token": "[SEP]",
   "mask_token": "[MASK]",
-  "name_or_path": "tner_ckpt/conll2003_deberta_v3_large/best_model",
+  "name_or_path": "microsoft/deberta-v3-large",
   "pad_token": "[PAD]",
   "sep_token": "[SEP]",
   "sp_model_kwargs": {},

trainer_config.json

@@ -0,0 +1 @@
+{"dataset": ["tner/conll2003"], "dataset_split": "train", "dataset_name": null, "local_dataset": null, "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": null, "lr_warmup_step_ratio": 0.1, "max_grad_norm": 10.0}