model update

README.md

@@ -0,0 +1,144 @@
+---
+datasets:
+- mit_movie_trivia
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/roberta-large-mit-movie-trivia
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: mit_movie_trivia
+      type: mit_movie_trivia
+      args: mit_movie_trivia
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.7284025200655909
+    - name: Precision
+      type: precision
+      value: 0.7151330283002881
+    - name: Recall
+      type: recall
+      value: 0.7421737601125572
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.6502255723148889
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.6457158565124362
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.6578012664661943
+    - 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/roberta-large-mit-movie-trivia
+
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-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): 0.7284025200655909
+- Precision (micro): 0.7151330283002881
+- Recall (micro): 0.7421737601125572
+- F1 (macro): 0.6502255723148889
+- Precision (macro): 0.6457158565124362
+- Recall (macro): 0.6578012664661943
+
+The per-entity breakdown of the F1 score on the test set are below:
+- actor: 0.9557453416149068
+- award: 0.41726618705035967
+- character_name: 0.7467105263157895
+- date: 0.9668674698795181
+- director: 0.9148936170212766
+- genre: 0.7277079593058049
+- opinion: 0.43478260869565216
+- origin: 0.28846153846153844
+- plot: 0.5132575757575758
+- quote: 0.8387096774193549
+- relationship: 0.5697329376854599
+- soundtrack: 0.42857142857142855 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.718570586211627, 0.7387631655667131]
+    - 95%: [0.7170135350354089, 0.7412372838115527] 
+- F1 (macro): 
+    - 90%: [0.718570586211627, 0.7387631655667131]
+    - 95%: [0.7170135350354089, 0.7412372838115527] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-mit-movie-trivia/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/roberta-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/roberta-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: roberta-large
+ - crf: True
+ - max_length: 128
+ - epoch: 15
+ - batch_size: 64
+ - lr: 1e-05
+ - random_seed: 42
+ - gradient_accumulation_steps: 1
+ - 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/roberta-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.",
+}
+
+```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "tner_ckpt/mit_movie_trivia_roberta_large/best_model",
+  "_name_or_path": "tner_ckpt/mit_movie_trivia_roberta_large/model_ayzfwn/epoch_5",
   "architectures": [
     "RobertaForTokenClassification"
   ],

eval/metric.json

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+{"micro/f1": 0.7284025200655909, "micro/f1_ci": {"90": [0.718570586211627, 0.7387631655667131], "95": [0.7170135350354089, 0.7412372838115527]}, "micro/recall": 0.7421737601125572, "micro/precision": 0.7151330283002881, "macro/f1": 0.6502255723148889, "macro/f1_ci": {"90": [0.6187041261525986, 0.6787182709337801], "95": [0.6109966715241302, 0.6849815706753698]}, "macro/recall": 0.6578012664661943, "macro/precision": 0.6457158565124362, "per_entity_metric": {"actor": {"f1": 0.9557453416149068, "f1_ci": {"90": [0.9467089142451703, 0.96457832207151], "95": [0.9446998104303214, 0.9663849104164107]}, "precision": 0.945468509984639, "recall": 0.966248037676609}, "award": {"f1": 0.41726618705035967, "f1_ci": {"90": [0.3174100863974281, 0.5303072158044535], "95": [0.3039128151260504, 0.5454545454545454]}, "precision": 0.3972602739726027, "recall": 0.4393939393939394}, "character_name": {"f1": 0.7467105263157895, "f1_ci": {"90": [0.7048839637035254, 0.7844453199853147], "95": [0.6974255691768825, 0.7915889898024219]}, "precision": 0.6984615384615385, "recall": 0.8021201413427562}, "date": {"f1": 0.9668674698795181, "f1_ci": {"90": [0.9560524286815729, 0.9766113703127701], "95": [0.9539611338144509, 0.978115674127837]}, "precision": 0.9625187406296851, "recall": 0.9712556732223904}, "director": {"f1": 0.9148936170212766, "f1_ci": {"90": [0.8969187934915231, 0.9319515648084927], "95": [0.8928550616050616, 0.9344502419864533]}, "precision": 0.9192399049881235, "recall": 0.9105882352941177}, "genre": {"f1": 0.7277079593058049, "f1_ci": {"90": [0.7030853825730076, 0.7531861349576963], "95": [0.6999361091231079, 0.7563387936006287]}, "precision": 0.6893424036281179, "recall": 0.770595690747782}, "opinion": {"f1": 0.43478260869565216, "f1_ci": {"90": [0.3816325514218835, 0.4874672405087639], "95": [0.37323313215722687, 0.4961574784559085]}, "precision": 0.4336734693877551, "recall": 0.4358974358974359}, "origin": {"f1": 0.28846153846153844, "f1_ci": {"90": [0.23671497584541065, 0.3412573724871241], "95": [0.22724880382775117, 0.3492127496159754]}, "precision": 0.26548672566371684, "recall": 0.3157894736842105}, "plot": {"f1": 0.5132575757575758, "f1_ci": {"90": [0.49135588028061145, 0.5333437337493498], "95": [0.48749443384223917, 0.5387301802457543]}, "precision": 0.5109993714644877, "recall": 0.5155358275206088}, "quote": {"f1": 0.8387096774193549, "f1_ci": {"90": [0.7469519870526883, 0.9213483146067417], "95": [0.7272727272727273, 0.9334042553191492]}, "precision": 0.8478260869565217, "recall": 0.8297872340425532}, "relationship": {"f1": 0.5697329376854599, "f1_ci": {"90": [0.5093859929645027, 0.6280584452098124], "95": [0.49858144263243415, 0.6380924033610567]}, "precision": 0.5783132530120482, "recall": 0.5614035087719298}, "soundtrack": {"f1": 0.42857142857142855, "f1_ci": {"90": [0.1111111111111111, 0.7142857142857143], "95": [0.0, 0.7777777777777777]}, "precision": 0.5, "recall": 0.375}}}

eval/metric_span.json

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+{"micro/f1": 0.749525289142068, "micro/f1_ci": {"90": [0.7397126154825829, 0.7597425112279943], "95": [0.7379706607775025, 0.7623178184832154]}, "micro/recall": 0.7636299683432993, "micro/precision": 0.7359322033898306, "macro/f1": 0.749525289142068, "macro/f1_ci": {"90": [0.7397126154825829, 0.7597425112279943], "95": [0.7379706607775025, 0.7623178184832154]}, "macro/recall": 0.7636299683432993, "macro/precision": 0.7359322033898306}

pytorch_model.bin

@@ -1,3 +1,3 @@
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-oid sha256:f5c42436f7538af5cd324260a79cf87c777eef3de550c2b60230e703893b3e15
-size 1417474161
+oid sha256:5a2cc41c6f7f179b759608e8acb7bb682e92ee0c5813bed07f3794294ae3e402
+size 1417479601

tokenizer_config.json

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

trainer_config.json

@@ -0,0 +1 @@
+{"dataset": ["tner/mit_movie_trivia"], "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": 1e-07, "lr_warmup_step_ratio": 0.1, "max_grad_norm": 10.0}