model update

README.md

@@ -0,0 +1,122 @@
+---
+datasets:
+- bc5cdr
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/roberta-large-bc5cdr
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: bc5cdr
+      type: bc5cdr
+      args: bc5cdr
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.8840696387239609
+    - name: Precision
+      type: precision
+      value: 0.8728266269249876
+    - name: Recall
+      type: recall
+      value: 0.8956060760526048
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.8797360472482783
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.8684274142690976
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.8913672531528037
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.886283586595552
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.8750124192747144
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.8978489142624121
+
+pipeline_tag: token-classification
+widget:
+- text: "Jacob Collier is a Grammy awarded artist from England."
+  example_title: "NER Example 1"
+---
+# tner/roberta-large-bc5cdr
+
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the 
+[tner/bc5cdr](https://huggingface.co/datasets/tner/bc5cdr) 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): 0.8840696387239609
+- Precision (micro): 0.8728266269249876
+- Recall (micro): 0.8956060760526048
+- F1 (macro): 0.8797360472482783
+- Precision (macro): 0.8684274142690976
+- Recall (macro): 0.8913672531528037
+
+The per-entity breakdown of the F1 score on the test set are below:
+- chemical: 0.9256943167187788
+- disease: 0.8337777777777777 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.878869501707946, 0.8890795634554179]
+    - 95%: [0.8776790106527211, 0.8897422640465147] 
+- F1 (macro): 
+    - 90%: [0.878869501707946, 0.8890795634554179]
+    - 95%: [0.8776790106527211, 0.8897422640465147] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-bc5cdr/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/roberta-large-bc5cdr/raw/main/eval/metric_span.json).
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+ - dataset: ['tner/bc5cdr']
+ - dataset_split: train
+ - dataset_name: None
+ - local_dataset: None
+ - model: roberta-large
+ - crf: True
+ - max_length: 128
+ - epoch: 15
+ - batch_size: 64
+ - lr: 1e-05
+ - random_seed: 42
+ - gradient_accumulation_steps: 1
+ - 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/roberta-large-bc5cdr/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/bc5cdr_roberta_large/best_model",
+  "_name_or_path": "tner_ckpt/bc5cdr_roberta_large/model_rcsnba/epoch_5",
   "architectures": [
     "RobertaForTokenClassification"
   ],

eval/metric.json

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+{"micro/f1": 0.8840696387239609, "micro/f1_ci": {"90": [0.878869501707946, 0.8890795634554179], "95": [0.8776790106527211, 0.8897422640465147]}, "micro/recall": 0.8956060760526048, "micro/precision": 0.8728266269249876, "macro/f1": 0.8797360472482783, "macro/f1_ci": {"90": [0.8744144067750624, 0.8846695860165229], "95": [0.8730734458935475, 0.8856738218616871]}, "macro/recall": 0.8913672531528037, "macro/precision": 0.8684274142690976, "per_entity_metric": {"chemical": {"f1": 0.9256943167187788, "f1_ci": {"90": [0.9200418390493498, 0.9307510614798177], "95": [0.9189182982927817, 0.9314566083883723]}, "precision": 0.9169247586081254, "recall": 0.9346332404828227}, "disease": {"f1": 0.8337777777777777, "f1_ci": {"90": [0.8248297919157508, 0.8425488073476383], "95": [0.8227259027600149, 0.8437044280627852]}, "precision": 0.8199300699300699, "recall": 0.8481012658227848}}}

eval/metric_span.json

@@ -0,0 +1 @@
+{"micro/f1": 0.886283586595552, "micro/f1_ci": {"90": [0.8813621339936485, 0.8911917188865727], "95": [0.88005486654077, 0.891949014446057]}, "micro/recall": 0.8978489142624121, "micro/precision": 0.8750124192747144, "macro/f1": 0.886283586595552, "macro/f1_ci": {"90": [0.8813621339936485, 0.8911917188865727], "95": [0.88005486654077, 0.891949014446057]}, "macro/recall": 0.8978489142624121, "macro/precision": 0.8750124192747144}

pytorch_model.bin

@@ -1,3 +1,3 @@
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-oid sha256:99a9fd565e76d1775117d515580168d79cac62b61740feb840c1211233a02b33
-size 1417392177
+oid sha256:d2f0359dee17dd9b432e06c7e3d10636daa1b8e234f563271ded7b732fe55330
+size 1417397617

tokenizer_config.json

@@ -6,7 +6,7 @@
   "errors": "replace",
   "mask_token": "<mask>",
   "model_max_length": 512,
-  "name_or_path": "tner_ckpt/bc5cdr_roberta_large/best_model",
+  "name_or_path": "tner_ckpt/bc5cdr_roberta_large/model_rcsnba/epoch_5",
   "pad_token": "<pad>",
   "sep_token": "</s>",
   "special_tokens_map_file": "tner_ckpt/bc5cdr_roberta_large/model_rcsnba/epoch_5/special_tokens_map.json",

trainer_config.json

@@ -0,0 +1 @@
+{"dataset": ["tner/bc5cdr"], "dataset_split": "train", "dataset_name": null, "local_dataset": null, "model": "roberta-large", "crf": true, "max_length": 128, "epoch": 15, "batch_size": 64, "lr": 1e-05, "random_seed": 42, "gradient_accumulation_steps": 1, "weight_decay": null, "lr_warmup_step_ratio": 0.1, "max_grad_norm": 10.0}