Text Classification
Transformers
ONNX
Safetensors
German
xlm-roberta
job-classification
german
sales-classification
taxonomy-sales
text-embeddings-inference
Instructions to use Ashybalka/xlm-roberta-taxonomy-sales-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ashybalka/xlm-roberta-taxonomy-sales-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ashybalka/xlm-roberta-taxonomy-sales-de")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ashybalka/xlm-roberta-taxonomy-sales-de") model = AutoModelForSequenceClassification.from_pretrained("Ashybalka/xlm-roberta-taxonomy-sales-de", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload model artifacts (11 files)
Browse files- .gitattributes +1 -0
- classification_report.txt +14 -0
- config.json +47 -0
- onnx-int8/model_quantized.onnx +3 -0
- onnx/model.onnx +3 -0
- pytorch/config.json +47 -0
- pytorch/model.safetensors +3 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +15 -0
- test_metrics.json +5 -0
- tokenizer.json +3 -0
- tokenizer_config.json +55 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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classification_report.txt
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precision recall f1-score support
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Business Development & Strategic Sales 0.852 0.868 0.860 53
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Field Sales & Outside Sales 0.925 0.876 0.900 226
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Inside Sales & Telesales 0.951 0.879 0.913 132
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Key Account & Account Management 0.879 0.892 0.885 65
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| 7 |
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Retail & Store Sales 0.982 0.982 0.982 500
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Sales Management & Leadership 0.760 0.864 0.809 44
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| 9 |
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Sales Operations & Support 0.759 0.936 0.838 47
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Technical Sales & Sales Engineering 0.824 0.875 0.848 48
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accuracy 0.926 1115
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macro avg 0.866 0.896 0.879 1115
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weighted avg 0.930 0.926 0.927 1115
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config.json
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{
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"architectures": [
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"XLMRobertaForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"dtype": "float32",
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "Business Development & Strategic Sales",
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"1": "Field Sales & Outside Sales",
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"2": "Inside Sales & Telesales",
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"3": "Key Account & Account Management",
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"4": "Retail & Store Sales",
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"5": "Sales Management & Leadership",
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"6": "Sales Operations & Support",
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"7": "Technical Sales & Sales Engineering"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"Business Development & Strategic Sales": 0,
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"Field Sales & Outside Sales": 1,
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"Inside Sales & Telesales": 2,
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"Key Account & Account Management": 3,
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"Retail & Store Sales": 4,
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"Sales Management & Leadership": 5,
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"Sales Operations & Support": 6,
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"Technical Sales & Sales Engineering": 7
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "xlm-roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_past": true,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"transformers_version": "4.57.6",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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}
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onnx-int8/model_quantized.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:76240033d820d26e4e77295e1d21d6ce3dc1cb66b37dec830856c062cab44b42
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size 278826977
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onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:d1855d283972e348fe933a3145e01585d375fcb718272ae3e950ce96eb23b507
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size 1112422168
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pytorch/config.json
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{
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"architectures": [
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"XLMRobertaForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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| 6 |
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"bos_token_id": 0,
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"classifier_dropout": null,
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"dtype": "float32",
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"eos_token_id": 2,
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"hidden_act": "gelu",
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| 11 |
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "Business Development & Strategic Sales",
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"1": "Field Sales & Outside Sales",
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"2": "Inside Sales & Telesales",
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"3": "Key Account & Account Management",
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"4": "Retail & Store Sales",
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"5": "Sales Management & Leadership",
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"6": "Sales Operations & Support",
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"7": "Technical Sales & Sales Engineering"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"Business Development & Strategic Sales": 0,
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"Field Sales & Outside Sales": 1,
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"Inside Sales & Telesales": 2,
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"Key Account & Account Management": 3,
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"Retail & Store Sales": 4,
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"Sales Management & Leadership": 5,
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"Sales Operations & Support": 6,
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"Technical Sales & Sales Engineering": 7
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "xlm-roberta",
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"num_attention_heads": 12,
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| 39 |
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"num_hidden_layers": 12,
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| 40 |
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"output_past": true,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"transformers_version": "4.57.6",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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}
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pytorch/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:b4be3cea70a0db636762023d0a6dfebb84f7e71b0ca62e09e38a14d32988045e
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size 1112223464
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sentencepiece.bpe.model
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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size 5069051
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special_tokens_map.json
ADDED
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{
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"bos_token": "<s>",
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": {
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"content": "<mask>",
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"lstrip": true,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"unk_token": "<unk>"
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}
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test_metrics.json
ADDED
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{
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"accuracy": 0.9264573991031391,
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"f1_macro": 0.8794732273709837,
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"f1_weighted": 0.9271627726484795
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}
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tokenizer.json
ADDED
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:8373f9cd3d27591e1924426bcc1c8799bc5a9affc4fc857982c5d66668dd1f41
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size 17082832
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tokenizer_config.json
ADDED
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{
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"added_tokens_decoder": {
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"0": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "</s>",
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| 21 |
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"lstrip": false,
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| 22 |
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"normalized": false,
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| 23 |
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"rstrip": false,
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| 24 |
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"single_word": false,
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| 25 |
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"special": true
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},
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| 27 |
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"3": {
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| 28 |
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"content": "<unk>",
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| 29 |
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"lstrip": false,
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| 30 |
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"normalized": false,
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| 31 |
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"rstrip": false,
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| 32 |
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"single_word": false,
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| 33 |
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"special": true
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},
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| 35 |
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"250001": {
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| 36 |
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"content": "<mask>",
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| 37 |
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"lstrip": true,
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| 38 |
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"normalized": false,
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| 39 |
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"rstrip": false,
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| 40 |
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"single_word": false,
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"special": true
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}
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},
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"bos_token": "<s>",
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| 45 |
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"clean_up_tokenization_spaces": false,
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| 46 |
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"cls_token": "<s>",
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| 47 |
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"eos_token": "</s>",
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| 48 |
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"extra_special_tokens": {},
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| 49 |
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"mask_token": "<mask>",
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| 50 |
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"model_max_length": 512,
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| 51 |
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"pad_token": "<pad>",
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| 52 |
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"sep_token": "</s>",
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| 53 |
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"tokenizer_class": "XLMRobertaTokenizer",
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| 54 |
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"unk_token": "<unk>"
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| 55 |
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}
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