For me, the model isn't working as intended.
For the input
text2 = "Proofpoint report mentions that the German-language messages were turned off once the UK messages were established, indicating a conscious effort to spread FluBot 446833e3f8b04d4c3c2d2288e456328266524e396adbfeba3769d00727481e80 in Android phones."
I get the following output:
Mention: Proofpoint report mentions that the German-language messages were turned off once the UK messages were established, indicating a conscious effort to spread FluBot 446833e3f8b04d4c3c2d2288e456328266524e396adbfeba3769d00727481e80 in Android phones., Class: LABEL_1, Start: 0, End: 246, Confidence: 0.62
Problem 1:
The whole text is labeled as one entity.
For bigger text, multiple big sections are detected.
Problem 2:
Not the original labels but Label_1 is returned.
For me, the model isn't working as intended.
For the input
text2 = "Proofpoint report mentions that the German-language messages were turned off once the UK messages were established, indicating a conscious effort to spread FluBot 446833e3f8b04d4c3c2d2288e456328266524e396adbfeba3769d00727481e80 in Android phones."I get the following output:
Mention: Proofpoint report mentions that the German-language messages were turned off once the UK messages were established, indicating a conscious effort to spread FluBot 446833e3f8b04d4c3c2d2288e456328266524e396adbfeba3769d00727481e80 in Android phones., Class: LABEL_1, Start: 0, End: 246, Confidence: 0.62
Problem 1:
The whole text is labeled as one entity.
For bigger text, multiple big sections are detected.
Problem 2:
Not the original labels but Label_1 is returned.