Update Model (#3)

Browse files

- readme: update model card (c2e7c321e078b05cff99535599332a5e6499de7a)
- model: add updated loss file (1f74c0235983fbb93fdd06452ecde963fa33bea6)
- model: add training log (95b24bec4f6185b4ab3bed9f98ba74cee689d433)
- model: add retrained version (d51f3d165e4da35fed3d228716d5f74fe7b048c3)

Co-authored-by: Stefan <[email protected]>

Files changed (4) hide show

README.md +9 -10
loss.tsv +151 -151
pytorch_model.bin +2 -2
training.log +0 -0

README.md CHANGED Viewed

@@ -3,10 +3,10 @@ tags:
 - flair
 - token-classification
 - sequence-tagger-model
-language:
-- en
-- de
-- fr
 - it
 - nl
 - pl
@@ -26,7 +26,7 @@ widget:
 This is the default multilingual universal part-of-speech tagging model that ships with [Flair](https://github.com/flairNLP/flair/).
-F1-Score: **98,47** (12 UD Treebanks covering English, German, French, Italian, Dutch, Polish, Spanish, Swedish, Danish, Norwegian, Finnish and Czech)
 Predicts universal POS tags:
@@ -94,14 +94,14 @@ Token[6]: "say" → VERB (0.9998)
 Token[7]: "." → PUNCT (1.0)
 ```
-So, the words "*Ich*" and "*they*" are labeled as **pronouns** (PRON), while "*liebe*" and "*say*" are labeled as **verbs** (VERB) in the multilingual sentence "*Ich liebe Berlin, as they say*".
 ---
 ### Training: Script to train this model
-The following Flair script was used to train this model:
 ```python
 from flair.data import MultiCorpus
@@ -129,11 +129,10 @@ corpus = MultiCorpus([
 tag_type = 'upos'
 # 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 = [
     # contextual string embeddings, forward
     FlairEmbeddings('multi-forward'),
@@ -141,7 +140,7 @@ embedding_types = [
     FlairEmbeddings('multi-backward'),
 ]
-# embedding stack consists of Flair and GloVe embeddings
 embeddings = StackedEmbeddings(embeddings=embedding_types)
 # 5. initialize sequence tagger

 - flair
 - token-classification
 - sequence-tagger-model
+language:
+- en
+- de
+- fr
 - it
 - nl
 - pl
 This is the default multilingual universal part-of-speech tagging model that ships with [Flair](https://github.com/flairNLP/flair/).
+F1-Score: **96.87** (12 UD Treebanks covering English, German, French, Italian, Dutch, Polish, Spanish, Swedish, Danish, Norwegian, Finnish and Czech)
 Predicts universal POS tags:
 Token[7]: "." → PUNCT (1.0)
 ```
+So, the words "*Ich*" and "*they*" are labeled as **pronouns** (PRON), while "*liebe*" and "*say*" are labeled as **verbs** (VERB) in the multilingual sentence "*Ich liebe Berlin, as they say*".
 ---
 ### Training: Script to train this model
+The following Flair script was used to train this model:
 ```python
 from flair.data import MultiCorpus
 tag_type = 'upos'
 # 3. make the tag dictionary from the corpus
+tag_dictionary = corpus.make_label_dictionary(label_type=tag_type)
 # 4. initialize each embedding we use
 embedding_types = [
     # contextual string embeddings, forward
     FlairEmbeddings('multi-forward'),
     FlairEmbeddings('multi-backward'),
 ]
+# embedding stack consists of Flair embeddings
 embeddings = StackedEmbeddings(embeddings=embedding_types)
 # 5. initialize sequence tagger

loss.tsv CHANGED Viewed

@@ -1,151 +1,151 @@
-EPOCH	TIMESTAMP	BAD_EPOCHS	LEARNING_RATE	TRAIN_LOSS	TRAIN_PRECISION	TRAIN_RECALL	TRAIN_ACCURACY	TRAIN_F-SCORE	DEV_LOSS	DEV_PRECISION	DEV_RECALL	DEV_ACCURACY	DEV_F-SCORE	TEST_LOSS	TEST_PRECISION	TEST_RECALL	TEST_ACCURACY	TEST_F-SCORE
-0	11:12:55	0	0.1000	0.7210021345162283		_	_	_	_	_		_	_	_	_	_	0.8481	0.8481	0.8481	0.8481
-1	11:36:23	0	0.1000	0.4978160696237081		_	_	_	_	_		_	_	_	_	_	0.878	0.878	0.878	0.878
-2	11:59:52	0	0.1000	0.43429711483610006		_	_	_	_	_		_	_	_	_	_	0.8967	0.8967	0.8967	0.8967
-3	12:23:17	0	0.1000	0.39905918871464724		_	_	_	_	_		_	_	_	_	_	0.904	0.904	0.904	0.904
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+EPOCH	TIMESTAMP	LEARNING_RATE	TRAIN_LOSS
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