Update README.md

README.md

@@ -2,4 +2,54 @@
 language: en
 license: apache-2.0
 datasets:
-- AQMAR
+- AQMAR
+---
+# Info
+Training was conducted over 94 epochs, using a linear decaying learning rate of 2e-05, and a total batch size of 32.
+11
+12
+
+Results:
+- F1-score (micro) 0.8666
+- F1-score (macro) 0.8488
+
+By class:
+LOC        tp: 539 - fp: 51 - fn: 68 - precision: 0.9136 - recall: 0.8880 - f1-score: 0.9006
+MISC       tp: 408 - fp: 57 - fn: 89 - precision: 0.8774 - recall: 0.8209 - f1-score: 0.8482
+ORG        tp: 167 - fp: 43 - fn: 64 - precision: 0.7952 - recall: 0.7229 - f1-score: 0.7574
+PER        tp: 501 - fp: 65 - fn: 60 - precision: 0.8852 - recall: 0.8930 - f1-score: 0.8891
+
+
+---
+
+```
+2020-10-27 12:05:47,801 Model: "SequenceTagger(
+  (embeddings): StackedEmbeddings(
+    (list_embedding_0): WordEmbeddings('glove')
+    (list_embedding_1): FlairEmbeddings(
+      (lm): LanguageModel(
+        (drop): Dropout(p=0.1, inplace=False)
+        (encoder): Embedding(7125, 100)
+        (rnn): LSTM(100, 2048)
+        (decoder): Linear(in_features=2048, out_features=7125, bias=True)
+      )
+    )
+    (list_embedding_2): FlairEmbeddings(
+      (lm): LanguageModel(
+        (drop): Dropout(p=0.1, inplace=False)
+        (encoder): Embedding(7125, 100)
+        (rnn): LSTM(100, 2048)
+        (decoder): Linear(in_features=2048, out_features=7125, bias=True)
+      )
+    )
+  )
+  (word_dropout): WordDropout(p=0.05)
+  (locked_dropout): LockedDropout(p=0.5)
+  (embedding2nn): Linear(in_features=4196, out_features=4196, bias=True)
+  (rnn): LSTM(4196, 256, batch_first=True, bidirectional=True)
+  (linear): Linear(in_features=512, out_features=15, bias=True)
+  (beta): 1.0
+  (weights): None
+  (weight_tensor) None
+  
+ ```