First model version

README.md

@@ -1,3 +1,94 @@
----
-license: mit
----
+---
+tags:
+- flair
+- hunflair
+- token-classification
+- sequence-tagger-model
+language: en
+widget:
+- text: "It contains a functional GCGGCGGCG Egr-1-binding site"
+---
+
+## HunFlair model for Transcription Factor Binding Site (TFBS)
+
+[HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for TFBS entity.
+
+
+Predicts 1 tag:
+
+| **tag**                        | **meaning** |
+|---------------------------------|-----------|
+| Tfbs         | DNA region bound by transcription factor | 
+
+---
+
+### 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
+# for biomedical-specific tokenization:
+# from flair.tokenization import SciSpacyTokenizer
+
+# load tagger
+tagger = SequenceTagger.load("regel-corpus/hunflair-tfbs")
+
+text = "We found that Egr-1 specifically binds to the PTEN 5' untranslated region, which contains a functional GCGGCGGCG Egr-1-binding site."
+
+# make example sentence
+sentence = Sentence(text)
+
+# for biomedical-specific tokenization:
+# sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer())
+
+# 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 [19,20,21]: "GCGGCGGCG Egr-1-binding site"   [− Labels: Tfbs (0.9631)]
+```
+
+So, the entity "*GCGGCGGCG Egr-1-binding site*" is found in the sentence. 
+
+Alternatively download all models locally and use the `MultiTagger` class.
+
+```python
+from flair.models import MultiTagger
+
+tagger = [
+'./models/hunflair-promoter/pytorch_model.bin',
+'./models/hunflair-enhancer/pytorch_model.bin',
+'./models/hunflair-tfbs/pytorch_model.bin',
+]
+
+tagger = MultiTagger.load(['./models/hunflair-'])
+
+tagger.predict(sentence)
+```
+
+---
+
+### Cite
+
+Please cite the following paper when using this model.
+
+```
+TODO
+```
+
+

loss.tsv

@@ -0,0 +1,101 @@
+EPOCH	TIMESTAMP	BAD_EPOCHS	LEARNING_RATE	TRAIN_LOSS
+1	11:10:11	0	0.1000	0.14710011079536267
+2	11:10:38	0	0.1000	0.052713516247065106
+3	11:11:08	0	0.1000	0.04170313093876623
+4	11:11:37	0	0.1000	0.03502531913431957
+5	11:12:05	0	0.1000	0.033235160847185675
+6	11:12:36	0	0.1000	0.02798608590571261
+7	11:13:05	1	0.1000	0.029335829712453517
+8	11:13:35	0	0.1000	0.027881322689149987
+9	11:14:04	0	0.1000	0.018479608349992186
+10	11:14:32	1	0.1000	0.02018662912365272
+11	11:15:00	0	0.1000	0.017519669068933433
+12	11:15:29	0	0.1000	0.017486945031318618
+13	11:15:59	0	0.1000	0.016083299853363285
+14	11:16:29	0	0.1000	0.013231104631704026
+15	11:17:00	1	0.1000	0.014683758352898918
+16	11:17:30	0	0.1000	0.012625955455249794
+17	11:18:01	0	0.1000	0.012044876115490816
+18	11:18:29	0	0.1000	0.01028024372570282
+19	11:18:58	1	0.1000	0.010979479036436342
+20	11:19:28	2	0.1000	0.011138790193971255
+21	11:19:57	3	0.1000	0.010698116323899108
+22	11:20:26	0	0.1000	0.009946761713055951
+23	11:20:57	0	0.1000	0.009936871515714868
+24	11:21:29	0	0.1000	0.009308915260079325
+25	11:21:59	0	0.1000	0.008796980498815181
+26	11:22:28	0	0.1000	0.008343673869569414
+27	11:22:57	0	0.1000	0.0073019944492553265
+28	11:23:25	0	0.1000	0.007136161202930401
+29	11:23:53	1	0.1000	0.007951276040687372
+30	11:24:23	0	0.1000	0.007115239192906809
+31	11:24:53	0	0.1000	0.004631937972905484
+32	11:25:23	1	0.1000	0.005918597149124509
+33	11:25:52	2	0.1000	0.006554636867261261
+34	11:26:23	3	0.1000	0.006275285747952336
+35	11:26:53	4	0.1000	0.007606807318433906
+36	11:27:23	0	0.0500	0.003300808944614297
+37	11:27:53	1	0.0500	0.0035301452838223347
+38	11:28:24	2	0.0500	0.00373693162841379
+39	11:28:54	0	0.0500	0.0024590705298489033
+40	11:29:24	1	0.0500	0.002555141605089226
+41	11:29:54	2	0.0500	0.003140753947428485
+42	11:30:24	0	0.0500	0.0022883215824823166
+43	11:30:53	1	0.0500	0.0027966012533954332
+44	11:31:22	0	0.0500	0.0020993295440013752
+45	11:31:51	1	0.0500	0.002740449417759927
+46	11:32:20	0	0.0500	0.002064300411742808
+47	11:32:51	1	0.0500	0.0021202283643759357
+48	11:33:22	0	0.0500	0.0019746542345450225
+49	11:33:52	1	0.0500	0.0024491152056052724
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+51	11:34:53	0	0.0500	0.0014394055756139811
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+53	11:35:53	2	0.0500	0.0022499665559356547
+54	11:36:24	3	0.0500	0.0018459306494895864
+55	11:36:54	4	0.0500	0.0016427611877301478
+56	11:37:26	1	0.0250	0.0015329277878862626
+57	11:37:55	0	0.0250	0.001336870801220413
+58	11:38:25	0	0.0250	0.0008539672446812107
+59	11:38:56	1	0.0250	0.0010773914105697013
+60	11:39:25	2	0.0250	0.0011901499907322449
+61	11:39:55	3	0.0250	0.0008571102830465054
+62	11:40:25	4	0.0250	0.0010969846457892516
+63	11:40:54	0	0.0125	0.0007889307362559138
+64	11:41:22	0	0.0125	0.0007578928750069597
+65	11:41:51	1	0.0125	0.0013097398813870695
+66	11:42:20	0	0.0125	0.0006851142786664053
+67	11:42:48	1	0.0125	0.0008955751240574359
+68	11:43:16	2	0.0125	0.0010339341863628265
+69	11:43:46	3	0.0125	0.0009957815745854383
+70	11:44:14	4	0.0125	0.0008350513470559088
+71	11:44:44	1	0.0063	0.0007752945301019462
+72	11:45:12	2	0.0063	0.0008474854508743485
+73	11:45:42	0	0.0063	0.0005586575015901546
+74	11:46:13	0	0.0063	0.0005568093736224873
+75	11:46:42	1	0.0063	0.0007954383216220868
+76	11:47:11	2	0.0063	0.0007537361897772951
+77	11:47:40	3	0.0063	0.0006539163257362983
+78	11:48:09	4	0.0063	0.0007293145411753851
+79	11:48:37	1	0.0031	0.0007481573379829859
+80	11:49:06	2	0.0031	0.0007069956713799059
+81	11:49:35	3	0.0031	0.0006705211829076631
+82	11:50:04	4	0.0031	0.0013279289754432272
+83	11:50:33	1	0.0016	0.0011473284043027811
+84	11:51:02	0	0.0016	0.0005220935390625203
+85	11:51:30	1	0.0016	0.0010433767265242975
+86	11:52:00	2	0.0016	0.0010116299189231366
+87	11:52:29	3	0.0016	0.000820558251712016
+88	11:52:58	4	0.0016	0.000658769017019137
+89	11:53:27	1	0.0008	0.0006596607302553888
+90	11:53:56	2	0.0008	0.0009832041381581406
+91	11:54:26	3	0.0008	0.0007839900746833205
+92	11:54:56	4	0.0008	0.000751059051643154
+93	11:55:25	1	0.0004	0.0008921829651493708
+94	11:55:55	2	0.0004	0.0007218841383679459
+95	11:56:26	0	0.0004	0.000456431968685245
+96	11:56:56	1	0.0004	0.0009873492483496796
+97	11:57:25	2	0.0004	0.0006661835511418295
+98	11:57:54	3	0.0004	0.0009475974411766899
+99	11:58:24	4	0.0004	0.0009786815036461967
+100	11:58:53	1	0.0002	0.0007292146594788754

pytorch_model.bin

@@ -0,0 +1,3 @@
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+oid sha256:9a335f97fa927c240e7fe5eaa035e383a28ece7c9f3d474e283d41ba1e52d6f6
+size 1104819835