model update
Browse files- README.md +176 -0
- eval/metric.json +0 -1
- eval/metric.test_2020.json +1 -0
- eval/metric.test_2021.json +1 -0
- eval/metric_span.test_2020.json +1 -0
- eval/metric_span.test_2021.json +1 -0
- eval/prediction.2020.dev.json +0 -0
- eval/prediction.2020.test.json +0 -0
- eval/prediction.2021.test.json +0 -0
- trainer_config.json +1 -1
README.md
ADDED
@@ -0,0 +1,176 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
datasets:
|
3 |
+
- tner/tweetner7
|
4 |
+
metrics:
|
5 |
+
- f1
|
6 |
+
- precision
|
7 |
+
- recall
|
8 |
+
model-index:
|
9 |
+
- name: tner/twitter-roberta-base-dec2021-tweetner7-2020
|
10 |
+
results:
|
11 |
+
- task:
|
12 |
+
name: Token Classification
|
13 |
+
type: token-classification
|
14 |
+
dataset:
|
15 |
+
name: tner/tweetner7/test_2021
|
16 |
+
type: tner/tweetner7/test_2021
|
17 |
+
args: tner/tweetner7/test_2021
|
18 |
+
metrics:
|
19 |
+
- name: F1
|
20 |
+
type: f1
|
21 |
+
value: 0.6417969860676713
|
22 |
+
- name: Precision
|
23 |
+
type: precision
|
24 |
+
value: 0.6314199395770392
|
25 |
+
- name: Recall
|
26 |
+
type: recall
|
27 |
+
value: 0.6525208140610546
|
28 |
+
- name: F1 (macro)
|
29 |
+
type: f1_macro
|
30 |
+
value: 0.5950190138355756
|
31 |
+
- name: Precision (macro)
|
32 |
+
type: precision_macro
|
33 |
+
value: 0.5844336783514947
|
34 |
+
- name: Recall (macro)
|
35 |
+
type: recall_macro
|
36 |
+
value: 0.6100191042323923
|
37 |
+
- name: F1 (entity span)
|
38 |
+
type: f1_entity_span
|
39 |
+
value: 0.77377161055505
|
40 |
+
- name: Precision (entity span)
|
41 |
+
type: precision_entity_span
|
42 |
+
value: 0.7612174107642385
|
43 |
+
- name: Recall (entity span)
|
44 |
+
type: recall_entity_span
|
45 |
+
value: 0.7867468486180178
|
46 |
+
- task:
|
47 |
+
name: Token Classification
|
48 |
+
type: token-classification
|
49 |
+
dataset:
|
50 |
+
name: tner/tweetner7/test_2020
|
51 |
+
type: tner/tweetner7/test_2020
|
52 |
+
args: tner/tweetner7/test_2020
|
53 |
+
metrics:
|
54 |
+
- name: F1
|
55 |
+
type: f1
|
56 |
+
value: 0.6535560344827587
|
57 |
+
- name: Precision
|
58 |
+
type: precision
|
59 |
+
value: 0.6795518207282913
|
60 |
+
- name: Recall
|
61 |
+
type: recall
|
62 |
+
value: 0.6294758692267773
|
63 |
+
- name: F1 (macro)
|
64 |
+
type: f1_macro
|
65 |
+
value: 0.6112036126522273
|
66 |
+
- name: Precision (macro)
|
67 |
+
type: precision_macro
|
68 |
+
value: 0.6366190072656497
|
69 |
+
- name: Recall (macro)
|
70 |
+
type: recall_macro
|
71 |
+
value: 0.5931815043549611
|
72 |
+
- name: F1 (entity span)
|
73 |
+
type: f1_entity_span
|
74 |
+
value: 0.7636755591484775
|
75 |
+
- name: Precision (entity span)
|
76 |
+
type: precision_entity_span
|
77 |
+
value: 0.7942825112107623
|
78 |
+
- name: Recall (entity span)
|
79 |
+
type: recall_entity_span
|
80 |
+
value: 0.7353399065905553
|
81 |
+
|
82 |
+
pipeline_tag: token-classification
|
83 |
+
widget:
|
84 |
+
- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {{@Herbie Hancock@}} via {{USERNAME}} link below: {{URL}}"
|
85 |
+
example_title: "NER Example 1"
|
86 |
+
---
|
87 |
+
# tner/twitter-roberta-base-dec2021-tweetner7-2020
|
88 |
+
|
89 |
+
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the
|
90 |
+
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` split).
|
91 |
+
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
|
92 |
+
for more detail). It achieves the following results on the test set of 2021:
|
93 |
+
- F1 (micro): 0.6417969860676713
|
94 |
+
- Precision (micro): 0.6314199395770392
|
95 |
+
- Recall (micro): 0.6525208140610546
|
96 |
+
- F1 (macro): 0.5950190138355756
|
97 |
+
- Precision (macro): 0.5844336783514947
|
98 |
+
- Recall (macro): 0.6100191042323923
|
99 |
+
|
100 |
+
|
101 |
+
|
102 |
+
The per-entity breakdown of the F1 score on the test set are below:
|
103 |
+
- corporation: 0.5161953727506428
|
104 |
+
- creative_work: 0.4749841671944269
|
105 |
+
- event: 0.43429109750353273
|
106 |
+
- group: 0.593413759373981
|
107 |
+
- location: 0.6431718061674009
|
108 |
+
- person: 0.8327532515112659
|
109 |
+
- product: 0.6703236423477785
|
110 |
+
|
111 |
+
For F1 scores, the confidence interval is obtained by bootstrap as below:
|
112 |
+
- F1 (micro):
|
113 |
+
- 90%: [0.6334648803400447, 0.651188450223803]
|
114 |
+
- 95%: [0.6314263719566943, 0.6528797499551452]
|
115 |
+
- F1 (macro):
|
116 |
+
- 90%: [0.6334648803400447, 0.651188450223803]
|
117 |
+
- 95%: [0.6314263719566943, 0.6528797499551452]
|
118 |
+
|
119 |
+
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/twitter-roberta-base-dec2021-tweetner7-2020/raw/main/eval/metric.json)
|
120 |
+
and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-dec2021-tweetner7-2020/raw/main/eval/metric_span.json).
|
121 |
+
|
122 |
+
### Usage
|
123 |
+
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
|
124 |
+
```shell
|
125 |
+
pip install tner
|
126 |
+
```
|
127 |
+
and activate model as below.
|
128 |
+
```python
|
129 |
+
from tner import TransformersNER
|
130 |
+
model = TransformersNER("tner/twitter-roberta-base-dec2021-tweetner7-2020")
|
131 |
+
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
|
132 |
+
```
|
133 |
+
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
|
134 |
+
|
135 |
+
### Training hyperparameters
|
136 |
+
|
137 |
+
The following hyperparameters were used during training:
|
138 |
+
- dataset: ['tner/tweetner7']
|
139 |
+
- dataset_split: train_2020
|
140 |
+
- dataset_name: None
|
141 |
+
- local_dataset: None
|
142 |
+
- model: cardiffnlp/twitter-roberta-base-dec2021
|
143 |
+
- crf: True
|
144 |
+
- max_length: 128
|
145 |
+
- epoch: 30
|
146 |
+
- batch_size: 32
|
147 |
+
- lr: 1e-05
|
148 |
+
- random_seed: 0
|
149 |
+
- gradient_accumulation_steps: 1
|
150 |
+
- weight_decay: 1e-07
|
151 |
+
- lr_warmup_step_ratio: 0.15
|
152 |
+
- max_grad_norm: 1
|
153 |
+
|
154 |
+
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/twitter-roberta-base-dec2021-tweetner7-2020/raw/main/trainer_config.json).
|
155 |
+
|
156 |
+
### Reference
|
157 |
+
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
|
158 |
+
|
159 |
+
```
|
160 |
+
|
161 |
+
@inproceedings{ushio-camacho-collados-2021-ner,
|
162 |
+
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
|
163 |
+
author = "Ushio, Asahi and
|
164 |
+
Camacho-Collados, Jose",
|
165 |
+
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
|
166 |
+
month = apr,
|
167 |
+
year = "2021",
|
168 |
+
address = "Online",
|
169 |
+
publisher = "Association for Computational Linguistics",
|
170 |
+
url = "https://aclanthology.org/2021.eacl-demos.7",
|
171 |
+
doi = "10.18653/v1/2021.eacl-demos.7",
|
172 |
+
pages = "53--62",
|
173 |
+
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.",
|
174 |
+
}
|
175 |
+
|
176 |
+
```
|
eval/metric.json
DELETED
@@ -1 +0,0 @@
|
|
1 |
-
{"2020.dev": {"micro/f1": 0.6370572207084468, "micro/f1_ci": {}, "micro/recall": 0.6107628004179728, "micro/precision": 0.6657175398633257, "macro/f1": 0.5818500927746258, "macro/f1_ci": {}, "macro/recall": 0.558634696207478, "macro/precision": 0.6115327302781756, "per_entity_metric": {"corporation": {"f1": 0.4693877551020408, "f1_ci": {}, "precision": 0.48677248677248675, "recall": 0.45320197044334976}, "creative_work": {"f1": 0.5144356955380578, "f1_ci": {}, "precision": 0.5664739884393064, "recall": 0.47115384615384615}, "event": {"f1": 0.3828920570264766, "f1_ci": {}, "precision": 0.4, "recall": 0.3671875}, "group": {"f1": 0.5622119815668202, "f1_ci": {}, "precision": 0.5893719806763285, "recall": 0.5374449339207048}, "location": {"f1": 0.6285714285714284, "f1_ci": {}, "precision": 0.5931372549019608, "recall": 0.6685082872928176}, "person": {"f1": 0.8676975945017182, "f1_ci": {}, "precision": 0.892226148409894, "recall": 0.8444816053511706}, "product": {"f1": 0.6477541371158392, "f1_ci": {}, "precision": 0.7527472527472527, "recall": 0.5684647302904564}}}, "2021.test": {"micro/f1": 0.6417969860676713, "micro/f1_ci": {"90": [0.6334648803400447, 0.651188450223803], "95": [0.6314263719566943, 0.6528797499551452]}, "micro/recall": 0.6525208140610546, "micro/precision": 0.6314199395770392, "macro/f1": 0.5950190138355756, "macro/f1_ci": {"90": [0.5855854380997908, 0.6046272067604185], "95": [0.5837653478173793, 0.606080435069187]}, "macro/recall": 0.6100191042323923, "macro/precision": 0.5844336783514947, "per_entity_metric": {"corporation": {"f1": 0.5161953727506428, "f1_ci": {"90": [0.4927218605886716, 0.5414831199725443], "95": [0.4870848768801742, 0.547844877649912]}, "precision": 0.48038277511961724, "recall": 0.5577777777777778}, "creative_work": {"f1": 0.4749841671944269, "f1_ci": {"90": [0.44315016015693665, 0.5054160340115397], "95": [0.4373215286098878, 0.5122643035682656]}, "precision": 0.44221698113207547, "recall": 0.5129958960328317}, "event": {"f1": 0.43429109750353273, "f1_ci": {"90": [0.4106041087816789, 0.45832656118533116], "95": [0.40788582234365367, 0.46288163349626044]}, "precision": 0.4501953125, "recall": 0.4194722474977252}, "group": {"f1": 0.593413759373981, "f1_ci": {"90": [0.5729480380392004, 0.6136009860444586], "95": [0.569351806541065, 0.6194689670779012]}, "precision": 0.5874757908327953, "recall": 0.5994729907773386}, "location": {"f1": 0.6431718061674009, "f1_ci": {"90": [0.6159219058444587, 0.6695603128406853], "95": [0.6090556482248325, 0.6734092369794471]}, "precision": 0.5853379152348225, "recall": 0.7136871508379888}, "person": {"f1": 0.8327532515112659, "f1_ci": {"90": [0.8230547559590614, 0.8433818253656356], "95": [0.8211029923755827, 0.8457833808000503]}, "precision": 0.827448125227521, "recall": 0.8381268436578171}, "product": {"f1": 0.6703236423477785, "f1_ci": {"90": [0.6478723110217235, 0.6911223620805819], "95": [0.6441389195232122, 0.6947141029343396]}, "precision": 0.717978848413631, "recall": 0.6286008230452675}}}, "2020.test": {"micro/f1": 0.6535560344827587, "micro/f1_ci": {"90": [0.6322110846801919, 0.6725291873276269], "95": [0.6284137294993963, 0.6757531238447321]}, "micro/recall": 0.6294758692267773, "micro/precision": 0.6795518207282913, "macro/f1": 0.6112036126522273, "macro/f1_ci": {"90": [0.5885812318209095, 0.6302682399052447], "95": [0.5851527157984716, 0.6350317949646983]}, "macro/recall": 0.5931815043549611, "macro/precision": 0.6366190072656497, "per_entity_metric": {"corporation": {"f1": 0.5960591133004925, "f1_ci": {"90": [0.5395060701236211, 0.6494117647058822], "95": [0.5253672975902609, 0.6620697527948355]}, "precision": 0.5627906976744186, "recall": 0.6335078534031413}, "creative_work": {"f1": 0.5341246290801188, "f1_ci": {"90": [0.47351444952608207, 0.5861169548093196], "95": [0.4651130219529672, 0.5969428423543331]}, "precision": 0.569620253164557, "recall": 0.5027932960893855}, "event": {"f1": 0.45136186770428016, "f1_ci": {"90": [0.39823686811875786, 0.5019271027404221], "95": [0.3909613718310647, 0.5127203344600605]}, "precision": 0.46586345381526106, "recall": 0.4377358490566038}, "group": {"f1": 0.5533453887884269, "f1_ci": {"90": [0.4990720961035416, 0.6060777264167093], "95": [0.48433459051724126, 0.6135325916276041]}, "precision": 0.6322314049586777, "recall": 0.4919614147909968}, "location": {"f1": 0.6361031518624641, "f1_ci": {"90": [0.5650739843960184, 0.6985154228855721], "95": [0.5545887898306531, 0.7110143602918171]}, "precision": 0.6032608695652174, "recall": 0.6727272727272727}, "person": {"f1": 0.8492678725236864, "f1_ci": {"90": [0.8247403953294568, 0.8714447733934279], "95": [0.8194677632518109, 0.8756164968824685]}, "precision": 0.8725663716814159, "recall": 0.8271812080536913}, "product": {"f1": 0.6581632653061223, "f1_ci": {"90": [0.6046445933517278, 0.7088196286472149], "95": [0.5933362116697867, 0.7185306985239963]}, "precision": 0.75, "recall": 0.5863636363636363}}}, "2021.test (span detection)": {"micro/f1": 0.77377161055505, "micro/f1_ci": {}, "micro/recall": 0.7867468486180178, "micro/precision": 0.7612174107642385, "macro/f1": 0.77377161055505, "macro/f1_ci": {}, "macro/recall": 0.7867468486180178, "macro/precision": 0.7612174107642385}, "2020.test (span detection)": {"micro/f1": 0.7636755591484775, "micro/f1_ci": {}, "micro/recall": 0.7353399065905553, "micro/precision": 0.7942825112107623, "macro/f1": 0.7636755591484775, "macro/f1_ci": {}, "macro/recall": 0.7353399065905553, "macro/precision": 0.7942825112107623}}
|
|
|
|
eval/metric.test_2020.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"micro/f1": 0.6535560344827587, "micro/f1_ci": {"90": [0.6322110846801919, 0.6725291873276269], "95": [0.6284137294993963, 0.6757531238447321]}, "micro/recall": 0.6294758692267773, "micro/precision": 0.6795518207282913, "macro/f1": 0.6112036126522273, "macro/f1_ci": {"90": [0.5885812318209095, 0.6302682399052447], "95": [0.5851527157984716, 0.6350317949646983]}, "macro/recall": 0.5931815043549611, "macro/precision": 0.6366190072656497, "per_entity_metric": {"corporation": {"f1": 0.5960591133004925, "f1_ci": {"90": [0.5395060701236211, 0.6494117647058822], "95": [0.5253672975902609, 0.6620697527948355]}, "precision": 0.5627906976744186, "recall": 0.6335078534031413}, "creative_work": {"f1": 0.5341246290801188, "f1_ci": {"90": [0.47351444952608207, 0.5861169548093196], "95": [0.4651130219529672, 0.5969428423543331]}, "precision": 0.569620253164557, "recall": 0.5027932960893855}, "event": {"f1": 0.45136186770428016, "f1_ci": {"90": [0.39823686811875786, 0.5019271027404221], "95": [0.3909613718310647, 0.5127203344600605]}, "precision": 0.46586345381526106, "recall": 0.4377358490566038}, "group": {"f1": 0.5533453887884269, "f1_ci": {"90": [0.4990720961035416, 0.6060777264167093], "95": [0.48433459051724126, 0.6135325916276041]}, "precision": 0.6322314049586777, "recall": 0.4919614147909968}, "location": {"f1": 0.6361031518624641, "f1_ci": {"90": [0.5650739843960184, 0.6985154228855721], "95": [0.5545887898306531, 0.7110143602918171]}, "precision": 0.6032608695652174, "recall": 0.6727272727272727}, "person": {"f1": 0.8492678725236864, "f1_ci": {"90": [0.8247403953294568, 0.8714447733934279], "95": [0.8194677632518109, 0.8756164968824685]}, "precision": 0.8725663716814159, "recall": 0.8271812080536913}, "product": {"f1": 0.6581632653061223, "f1_ci": {"90": [0.6046445933517278, 0.7088196286472149], "95": [0.5933362116697867, 0.7185306985239963]}, "precision": 0.75, "recall": 0.5863636363636363}}}
|
eval/metric.test_2021.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"micro/f1": 0.6417969860676713, "micro/f1_ci": {"90": [0.6334648803400447, 0.651188450223803], "95": [0.6314263719566943, 0.6528797499551452]}, "micro/recall": 0.6525208140610546, "micro/precision": 0.6314199395770392, "macro/f1": 0.5950190138355756, "macro/f1_ci": {"90": [0.5855854380997908, 0.6046272067604185], "95": [0.5837653478173793, 0.606080435069187]}, "macro/recall": 0.6100191042323923, "macro/precision": 0.5844336783514947, "per_entity_metric": {"corporation": {"f1": 0.5161953727506428, "f1_ci": {"90": [0.4927218605886716, 0.5414831199725443], "95": [0.4870848768801742, 0.547844877649912]}, "precision": 0.48038277511961724, "recall": 0.5577777777777778}, "creative_work": {"f1": 0.4749841671944269, "f1_ci": {"90": [0.44315016015693665, 0.5054160340115397], "95": [0.4373215286098878, 0.5122643035682656]}, "precision": 0.44221698113207547, "recall": 0.5129958960328317}, "event": {"f1": 0.43429109750353273, "f1_ci": {"90": [0.4106041087816789, 0.45832656118533116], "95": [0.40788582234365367, 0.46288163349626044]}, "precision": 0.4501953125, "recall": 0.4194722474977252}, "group": {"f1": 0.593413759373981, "f1_ci": {"90": [0.5729480380392004, 0.6136009860444586], "95": [0.569351806541065, 0.6194689670779012]}, "precision": 0.5874757908327953, "recall": 0.5994729907773386}, "location": {"f1": 0.6431718061674009, "f1_ci": {"90": [0.6159219058444587, 0.6695603128406853], "95": [0.6090556482248325, 0.6734092369794471]}, "precision": 0.5853379152348225, "recall": 0.7136871508379888}, "person": {"f1": 0.8327532515112659, "f1_ci": {"90": [0.8230547559590614, 0.8433818253656356], "95": [0.8211029923755827, 0.8457833808000503]}, "precision": 0.827448125227521, "recall": 0.8381268436578171}, "product": {"f1": 0.6703236423477785, "f1_ci": {"90": [0.6478723110217235, 0.6911223620805819], "95": [0.6441389195232122, 0.6947141029343396]}, "precision": 0.717978848413631, "recall": 0.6286008230452675}}}
|
eval/metric_span.test_2020.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"micro/f1": 0.7636755591484775, "micro/f1_ci": {}, "micro/recall": 0.7353399065905553, "micro/precision": 0.7942825112107623, "macro/f1": 0.7636755591484775, "macro/f1_ci": {}, "macro/recall": 0.7353399065905553, "macro/precision": 0.7942825112107623}
|
eval/metric_span.test_2021.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"micro/f1": 0.77377161055505, "micro/f1_ci": {}, "micro/recall": 0.7867468486180178, "micro/precision": 0.7612174107642385, "macro/f1": 0.77377161055505, "macro/f1_ci": {}, "macro/recall": 0.7867468486180178, "macro/precision": 0.7612174107642385}
|
eval/prediction.2020.dev.json
DELETED
The diff for this file is too large to render.
See raw diff
|
|
eval/prediction.2020.test.json
DELETED
The diff for this file is too large to render.
See raw diff
|
|
eval/prediction.2021.test.json
DELETED
The diff for this file is too large to render.
See raw diff
|
|
trainer_config.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"
|
|
|
1 |
+
{"dataset": ["tner/tweetner7"], "dataset_split": "train_2020", "dataset_name": null, "local_dataset": null, "model": "cardiffnlp/twitter-roberta-base-dec2021", "crf": true, "max_length": 128, "epoch": 30, "batch_size": 32, "lr": 1e-05, "random_seed": 0, "gradient_accumulation_steps": 1, "weight_decay": 1e-07, "lr_warmup_step_ratio": 0.15, "max_grad_norm": 1}
|