initial model commit

README.md

@@ -0,0 +1,145 @@
+---
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+language: en
+datasets:
+- conll2003
+inference: false
+---
+
+## English NER in Flair (fast model)
+
+This is the fast 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
+
+F1-Score: **92,92** (corrected CoNLL-03)
+
+Predicts 4 tags:
+
+| **tag**                        | **meaning** |
+|---------------------------------|-----------|
+| PER         | person name | 
+| LOC         | location name | 
+| ORG         | organization name | 
+| MISC         | other name | 
+
+Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
+
+---
+
+### Demo: How to use in Flair
+
+Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
+
+```python
+from flair.data import Sentence
+from flair.models import SequenceTagger
+
+# load tagger
+tagger = SequenceTagger.load("flair/ner-english-fast")
+
+# make example sentence
+sentence = Sentence("George Washington went to Washington")
+
+# predict NER tags
+tagger.predict(sentence)
+
+# print sentence
+print(sentence)
+
+# print predicted NER spans
+print('The following NER tags are found:')
+# iterate over entities and print
+for entity in sentence.get_spans('ner'):
+    print(entity)
+
+```
+
+This yields the following output:
+```
+Span [1,2]: "George Washington"   [− Labels: PER (0.9968)]
+Span [5]: "Washington"   [− Labels: LOC (0.9994)]
+```
+
+So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*". 
+
+
+---
+
+### Training: Script to train this model
+
+The following Flair script was used to train this model: 
+
+```python
+from flair.data import Corpus
+from flair.datasets import CONLL_03
+from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
+
+# 1. get the corpus
+corpus: Corpus = CONLL_03()
+
+# 2. what tag do we want to predict?
+tag_type = 'ner'
+
+# 3. make the tag dictionary from the corpus
+tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
+
+# 4. initialize each embedding we use
+embedding_types = [
+
+    # GloVe embeddings
+    WordEmbeddings('glove'),
+
+    # contextual string embeddings, forward
+    FlairEmbeddings('news-forward'),
+
+    # contextual string embeddings, backward
+    FlairEmbeddings('news-backward'),
+]
+
+# embedding stack consists of Flair and GloVe embeddings
+embeddings = StackedEmbeddings(embeddings=embedding_types)
+
+# 5. initialize sequence tagger
+from flair.models import SequenceTagger
+
+tagger = SequenceTagger(hidden_size=256,
+                        embeddings=embeddings,
+                        tag_dictionary=tag_dictionary,
+                        tag_type=tag_type)
+
+# 6. initialize trainer
+from flair.trainers import ModelTrainer
+
+trainer = ModelTrainer(tagger, corpus)
+
+# 7. run training
+trainer.train('resources/taggers/ner-english',
+              train_with_dev=True,
+              max_epochs=150)
+```
+
+
+
+---
+
+### Cite
+
+Please cite the following paper when using this model.
+
+```
+@inproceedings{akbik2018coling,
+  title={Contextual String Embeddings for Sequence Labeling},
+  author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
+  booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
+  pages     = {1638--1649},
+  year      = {2018}
+}
+```
+
+---
+
+### Issues?
+
+The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).

loss.tsv

@@ -0,0 +1,151 @@
+EPOCH	TIMESTAMP	BAD_EPOCHS	LEARNING_RATE	TRAIN_LOSS
+1	21:27:37	0	0.1000	3.576245712941583
+2	21:29:01	0	0.1000	1.6267465572285502
+3	21:30:26	0	0.1000	1.2932199398645117
+4	21:31:51	0	0.1000	1.1177818077274515
+5	21:33:15	0	0.1000	1.0296866451449032
+6	21:34:40	0	0.1000	0.9302731227554097
+7	21:36:05	0	0.1000	0.8873416783783254
+8	21:37:30	0	0.1000	0.8345710260769988
+9	21:38:55	0	0.1000	0.8028050488969193
+10	21:40:19	0	0.1000	0.769371804132869
+11	21:41:44	0	0.1000	0.7434782063989323
+12	21:43:09	0	0.1000	0.7134135925694357
+13	21:44:35	0	0.1000	0.697478834633963
+14	21:46:00	0	0.1000	0.654981232660858
+15	21:47:25	0	0.1000	0.6490681288363058
+16	21:48:51	0	0.1000	0.6350203718801465
+17	21:50:16	0	0.1000	0.6019481727735528
+18	21:51:41	0	0.1000	0.5960373881779895
+19	21:53:05	0	0.1000	0.571467437958227
+20	21:54:30	0	0.1000	0.5631542286682355
+21	21:55:56	0	0.1000	0.5591391156887329
+22	21:57:21	0	0.1000	0.5469877312926552
+23	21:58:46	0	0.1000	0.543492383406132
+24	22:00:10	0	0.1000	0.5150289349493724
+25	22:01:35	1	0.1000	0.5185878202554923
+26	22:03:00	0	0.1000	0.5032382932597701
+27	22:04:26	1	0.1000	0.5042508806844678
+28	22:05:51	0	0.1000	0.48771740151922915
+29	22:07:17	0	0.1000	0.47727425799622564
+30	22:08:42	1	0.1000	0.4816816206549919
+31	22:10:06	0	0.1000	0.46041105548509315
+32	22:11:46	1	0.1000	0.4666691600474753
+33	22:13:11	0	0.1000	0.4550447771915152
+34	22:14:36	0	0.1000	0.4482554853716983
+35	22:16:00	0	0.1000	0.4392374910980086
+36	22:17:26	1	0.1000	0.44741923635519004
+37	22:18:50	0	0.1000	0.4324368980206266
+38	22:20:15	0	0.1000	0.42393419299793395
+39	22:21:41	1	0.1000	0.4377899808644117
+40	22:23:06	0	0.1000	0.40397032364448415
+41	22:24:32	1	0.1000	0.4180835625274649
+42	22:25:59	2	0.1000	0.40589749664539776
+43	22:27:43	3	0.1000	0.4139155642707137
+44	22:29:08	0	0.1000	0.40055191476793983
+45	22:30:33	0	0.1000	0.3847549316013538
+46	22:31:59	1	0.1000	0.39164391398146936
+47	22:33:24	0	0.1000	0.378595719444035
+48	22:34:49	0	0.1000	0.37545035072142563
+49	22:36:15	1	0.1000	0.37854261922685406
+50	22:37:39	0	0.1000	0.3668022963065135
+51	22:39:04	0	0.1000	0.3653561896821366
+52	22:40:29	1	0.1000	0.36685169342009327
+53	22:41:55	0	0.1000	0.3547235012431688
+54	22:43:19	1	0.1000	0.35747438044393365
+55	22:44:44	2	0.1000	0.36117520110233675
+56	22:46:09	0	0.1000	0.3506846820251851
+57	22:47:35	0	0.1000	0.34869071834166593
+58	22:49:01	1	0.1000	0.34916296806422215
+59	22:50:26	0	0.1000	0.3443586528065461
+60	22:51:50	1	0.1000	0.34437141102986246
+61	22:53:15	2	0.1000	0.346735600452823
+62	22:54:40	0	0.1000	0.3406031290894445
+63	22:56:05	0	0.1000	0.3351760045448436
+64	22:57:29	0	0.1000	0.3322265410112052
+65	22:58:53	0	0.1000	0.32590263230796856
+66	23:00:18	0	0.1000	0.31927544255799883
+67	23:01:43	1	0.1000	0.3250151945018693
+68	23:03:08	2	0.1000	0.32256935213845744
+69	23:04:33	3	0.1000	0.3210709377463105
+70	23:05:58	4	0.1000	0.32127140860863124
+71	23:07:25	0	0.0500	0.30183341349416143
+72	23:09:15	0	0.0500	0.2842321318774661
+73	23:10:40	1	0.0500	0.2871976368412187
+74	23:12:05	0	0.0500	0.2702093680825415
+75	23:13:29	1	0.0500	0.27023764628964136
+76	23:14:55	0	0.0500	0.26883385820856576
+77	23:16:21	0	0.0500	0.26329286245605615
+78	23:17:46	0	0.0500	0.2628642472002325
+79	23:19:11	0	0.0500	0.259990004138856
+80	23:20:36	1	0.0500	0.27014149534457094
+81	23:22:01	0	0.0500	0.2593999018869068
+82	23:23:26	0	0.0500	0.2551785586283931
+83	23:24:51	0	0.0500	0.2534079396319163
+84	23:26:15	1	0.0500	0.2551872474671919
+85	23:27:40	2	0.0500	0.2594162785224145
+86	23:29:07	0	0.0500	0.2524074929990346
+87	23:30:32	0	0.0500	0.25090720037682146
+88	23:31:58	0	0.0500	0.23916956306069712
+89	23:33:22	0	0.0500	0.2356912124241832
+90	23:34:47	1	0.0500	0.24508511117081852
+91	23:36:11	2	0.0500	0.24216246373857123
+92	23:37:37	3	0.0500	0.23845135922767693
+93	23:39:02	4	0.0500	0.23764705532996716
+94	23:40:27	1	0.0250	0.24339046411782125
+95	23:41:51	0	0.0250	0.2290070934716282
+96	23:43:16	0	0.0250	0.2265663307065828
+97	23:44:41	1	0.0250	0.22918056114282034
+98	23:46:06	0	0.0250	0.2230998661982108
+99	23:47:30	0	0.0250	0.21604776231548453
+100	23:48:56	1	0.0250	0.23747224881773507
+101	23:50:20	2	0.0250	0.21889492183263543
+102	23:51:44	0	0.0250	0.2143383389220962
+103	23:53:08	1	0.0250	0.21810815311213838
+104	23:54:31	2	0.0250	0.21440149303761463
+105	23:55:54	0	0.0250	0.21424433980372887
+106	23:57:18	1	0.0250	0.2152267113988158
+107	23:58:41	0	0.0250	0.2141137710194799
+108	00:00:04	0	0.0250	0.21356280777556233
+109	00:01:27	1	0.0250	0.22050891868487188
+110	00:02:50	2	0.0250	0.21593614047558246
+111	00:04:13	0	0.0250	0.21179587707583664
+112	00:05:35	0	0.0250	0.20590584845388238
+113	00:07:03	1	0.0250	0.20764590038245992
+114	00:08:37	0	0.0250	0.2053749050143399
+115	00:10:00	1	0.0250	0.20565480036260206
+116	00:11:23	2	0.0250	0.20719853168518482
+117	00:12:45	3	0.0250	0.20710589055302023
+118	00:14:07	0	0.0250	0.19874572183323813
+119	00:15:30	1	0.0250	0.21330269343585154
+120	00:16:52	2	0.0250	0.20283179949450342
+121	00:18:15	3	0.0250	0.2028056960081375
+122	00:19:37	4	0.0250	0.20502184220601485
+123	00:20:59	0	0.0125	0.1984874309052395
+124	00:22:22	0	0.0125	0.19242043826210348
+125	00:23:44	1	0.0125	0.20257633792448648
+126	00:25:05	2	0.0125	0.19872382566144195
+127	00:26:28	3	0.0125	0.19759908735846418
+128	00:27:51	4	0.0125	0.20143914043526107
+129	00:29:15	1	0.0063	0.19428333055369462
+130	00:30:37	2	0.0063	0.19641075125317783
+131	00:31:59	3	0.0063	0.20426463074039056
+132	00:33:22	4	0.0063	0.1924995852374955
+133	00:34:44	0	0.0031	0.18798776621682733
+134	00:36:06	1	0.0031	0.19395236284295214
+135	00:37:28	2	0.0031	0.19617622008523608
+136	00:38:51	3	0.0031	0.1928124738314861
+137	00:40:13	4	0.0031	0.20205567409343358
+138	00:41:35	1	0.0016	0.19410402266473709
+139	00:42:58	2	0.0016	0.1933639193590306
+140	00:44:20	3	0.0016	0.1959089887670324
+141	00:45:43	4	0.0016	0.19630460231270216
+142	00:47:06	1	0.0008	0.19898560986252903
+143	00:48:32	2	0.0008	0.19213544053933287
+144	00:49:56	3	0.0008	0.18811244633095928
+145	00:51:18	4	0.0008	0.1902414702256269
+146	00:52:41	1	0.0004	0.189671358352975
+147	00:54:03	0	0.0004	0.1872933999741379
+148	00:55:25	1	0.0004	0.19926253187505505
+149	00:56:48	2	0.0004	0.19951951173664648
+150	00:58:10	0	0.0004	0.1853659376855704

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e5f0b76cd61ac10b332ed01d131c7fba58cfa93caf4736f09747b2dc1533399
+size 256731629