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README.md

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+---
+widget:
+
+- text: "My name is Mark and I live in London. I am a postgraduate student at Queen Mary University."
+language: 
+  - en
+license: mit
+---
+
+# Multilingual Hate Speech Classifier for Social Media Content
+
+A multilingual model for hate speech classification of social media content. The model is based on pre-trained multilingual representations from the XLM-T model (https://arxiv.org/abs/2104.12250) and was jointly fine-tuned on five languages, namely Arabic, Croatian, English, German and Slovenian. The test results on these five languages in terms of F1 score are as follows:
+
+| Language  |   F1   |
+|-----------|:------:|
+| Arabic    | 0.8704 |
+| Croatian  | 0.7226 |
+| English   | 0.7851 |
+| German    | 0.7826 |
+| Slovenian | 0.7596 |
+
+## Tokenizer
+
+During training the text was preprocessed using the original XLM-T tokenizer. The pretrained tokenizer files are included in this repository. We suggest the same tokenizer is used for inference.
+
+## Model output
+
+The model classifies each input into one of two distinct classes:
+* 0 - not-offensive
+* 1 - offensive

config.json

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+{
+  "_name_or_path": "/home/andrazp/cs_hs_robacofi/src/twitter-xlm-roberta-base/",
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

pytorch_model.bin

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sentencepiece.bpe.model

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