use proper model output dim

onnx_convert.py

@@ -1,9 +1,9 @@
-from transformers import AutoTokenizer, AutoModel
+from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
 import torch
 
 max_seq_length=128
 
-model = AutoModel.from_pretrained(".")
+model = AutoModelForSequenceClassification.from_pretrained(".")
 model.eval()
 
 inputs = {"input_ids": torch.ones(1, max_seq_length, dtype=torch.int64),

pytorch_model.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7ad93efc36fdce0ba568e5bae8db0ce8f2314b1a82bf46fc7732fac485414fd9
-size 90984263
+oid sha256:4394678219595bcaef56c0f47453247554c03550e904d55987fe2ec364ec6912
+size 90996922

test-small.json.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fb557251b12addb55d94af30120d121dfa6391e58bcc4a9aee0f1d35cc2ea1c8
-size 8522018

train-small.json.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9c7c14a8910a3a6c09421a08a84cfc0e74fd198d0aaf43ab2c39250a8ae4e4dd
-size 19430577