add model

config.json

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+{
+  "_name_or_path": "/content/drive/MyDrive/ColabModels/XROBERTA_SST2_SMALL/pytorch_model/",
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoModel": "modeling_roberta.XLMRobertaForSequenceClassification"
+  },
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "roberta": 1,
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 88361
+}

modeling_roberta.py

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+from torch import nn
+
+from transformers import XLMRobertaModel
+
+
+class XLMRobertaForSequenceClassification(XLMRobertaModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.classifier = nn.Linear(768, 2)
+
+    def forward(self, input_ids, attention_mask):
+        outputs = super(XLMRobertaForSequenceClassification, self).forward(input_ids=input_ids, attention_mask=attention_mask)
+        return self.classifier(outputs[1])

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f29b1c8bfd0655d557fb39fed07e9461c55ba2d436f891aaac54b4e9975b8fdb
+size 445547373