dleemiller
commited on
Upload 7 files
Browse files- CECorrelationEvaluator_sts-validation_results.csv +17 -0
- README.md +118 -0
- config.json +53 -0
- model.safetensors +3 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +952 -0
CECorrelationEvaluator_sts-validation_results.csv
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epoch,steps,Pearson_Correlation,Spearman_Correlation
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0,25,0.9197522040114249,0.9188164704261743
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0,50,0.9224662654226511,0.9206185126963341
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0,75,0.9234111089491247,0.9218807495877991
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0,-1,0.9220274359777092,0.9226046366291678
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1,25,0.9253334869538583,0.9233327016426005
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1,50,0.9260833551026787,0.9236030687487745
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1,75,0.9238369086012982,0.9216264413393251
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1,-1,0.9245986281821194,0.9220035922868607
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2,25,0.9243077961522106,0.9222928189866926
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2,50,0.9241624170199012,0.9220924222363452
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2,75,0.9248212609973545,0.9219553730087654
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2,-1,0.924600937519334,0.9215597182014892
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3,25,0.923844215153272,0.9212018896261087
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3,50,0.9237719874195967,0.9209844170343209
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3,75,0.9238568458607369,0.9210784615127332
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3,-1,0.9238772789028687,0.9210419327271685
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README.md
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# ModernBERT Cross-Encoder: Semantic Similarity (STS)
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Cross encoders are high performing encoder models that compare two texts and output a 0-1 score.
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I've found the `cross-encoders/roberta-large-stsb` model to be very useful in creating evaluators for LLM outputs.
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They're simple to use, fast and very accurate.
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Like many people, I was excited about the architecture and training uplift from the ModernBERT architecture (`answerdotai/ModernBERT-base`).
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So I've applied it to the stsb cross encoder, which is a very handy model. Additionally, I've added
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pretraining from my much larger semi-synthetic dataset `dleemiller/wiki-sim` that targets this kind of objective.
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---
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## Features
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- **High performing:** Achieves **Pearson: 0.9162** and **Spearman: 0.9122** on the STS-Benchmark test set.
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- **Efficient architecture:** Based on the ModernBERT-base design (149M parameters), offering faster inference speeds.
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- **Extended context length:** Processes sequences up to 8192 tokens, great for LLM output evals.
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- **Diversified training:** Pretrained on `dleemiller/wiki-sim` and fine-tuned on `sentence-transformers/stsb`.
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---
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## Performance
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| Model | STS-B Test Pearson | STS-B Test Spearman | Context Length | Parameters | Speed* |
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|--------------------------------|--------------------|---------------------|----------------|------------|---------|
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| **ModernCE-base-sts** | **0.9162** | **0.9122** | **8192** | 149M | **Fast** |
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| `roberta-large-stsb` | 0.9147 | 0.9115 | 512 | 355M | Slow |
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| `distilroberta-base-stsb` | 0.8792 | 0.8765 | 512 | 66M | Fast |
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---
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## Usage
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To use ModernCE for semantic similarity tasks, you can load the model with the Hugging Face `sentence-transformers` library:
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```python
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from sentence_transformers import CrossEncoder
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# Load ModernCE model
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model = CrossEncoder("dleemiller/ModernCE-base-sts")
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# Predict similarity scores for sentence pairs
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sentence_pairs = [
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("It's a wonderful day outside.", "It's so sunny today!"),
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("It's a wonderful day outside.", "He drove to work earlier."),
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]
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scores = model.predict(sentence_pairs)
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print(scores) # Outputs: array([0.9184, 0.0123], dtype=float32)
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```
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### Output
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The model returns similarity scores in the range `[0, 1]`, where higher scores indicate stronger semantic similarity.
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---
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## Training Details
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### Pretraining
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The model was pretrained on the `pair-score-sampled` subset of the [`dleemiller/wiki-sim`](https://huggingface.co/datasets/dleemiller/wiki-sim) dataset. This dataset provides diverse sentence pairs with semantic similarity scores, helping the model build a robust understanding of relationships between sentences.
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- **Classifier Dropout:** 0.3, to introduce regularization and reduce overfitting.
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- **Objective:** STS-B scores from `roberta-large-stsb`.
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### Fine-Tuning
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Fine-tuning was performed on the [`sentence-transformers/stsb`](https://huggingface.co/datasets/sentence-transformers/stsb) dataset.
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### Validation Results
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The model achieved the following test set performance after fine-tuning:
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- **Pearson Correlation:** 0.9162
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- **Spearman Correlation:** 0.9122
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Logs for training and evaluation are included in the [training logs](output/eval/sts-test-results.csv).
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---
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## Applications
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1. **Semantic Search:** Retrieve relevant documents or text passages based on query similarity.
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2. **Retrieval-Augmented Generation (RAG):** Enhance generative models by providing contextually relevant information.
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3. **Content Moderation:** Automatically classify or rank content based on similarity to predefined guidelines.
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4. **Code Search:** Leverage the model's ability to understand code and natural language for large-scale programming tasks.
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---
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## Model Card
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- **Architecture:** ModernBERT-base
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- **Tokenizer:** Custom tokenizer trained with modern techniques for long-context handling.
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- **Pretraining Data:** `dleemiller/wiki-sim (pair-score-sampled)`
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- **Fine-Tuning Data:** `sentence-transformers/stsb`
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---
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## Thank You
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Thanks to the AnswerAI team for providing the ModernBERT models, and the Sentence Transformers team for their leadership in transformer encoder models.
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---
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## Citation
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If you use this model in your research, please cite:
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```bibtex
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@misc{moderncestsb2025,
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author = {Miller, D. Lee},
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title = {ModernCE STS: An STS cross encoder model},
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year = {2025},
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publisher = {Hugging Face Hub},
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url = {https://huggingface.co/dleemiller/ModernCE-base-sts},
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}
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```
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---
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## License
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This model is licensed under the [MIT License](LICENSE).
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config.json
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{
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"_name_or_path": "output/wiki-sim-binary",
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"architectures": [
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"ModernBertForSequenceClassification"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 50281,
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"classifier_activation": "gelu",
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"classifier_bias": false,
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"classifier_dropout": 0.2,
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"classifier_pooling": "mean",
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"cls_token_id": 50281,
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"decoder_bias": true,
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"deterministic_flash_attn": false,
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"embedding_dropout": 0.0,
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"eos_token_id": 50282,
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"global_attn_every_n_layers": 3,
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"global_rope_theta": 160000.0,
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"gradient_checkpointing": false,
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"hidden_activation": "gelu",
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"hidden_size": 768,
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"id2label": {
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"0": "LABEL_0"
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},
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"initializer_cutoff_factor": 2.0,
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"initializer_range": 0.02,
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"intermediate_size": 1152,
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"label2id": {
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"LABEL_0": 0
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},
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"layer_norm_eps": 1e-05,
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"local_attention": 128,
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"local_rope_theta": 10000.0,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"mlp_dropout": 0.0,
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"model_type": "modernbert",
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"norm_bias": false,
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"norm_eps": 1e-05,
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"num_attention_heads": 12,
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"num_hidden_layers": 22,
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"pad_token_id": 50283,
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"position_embedding_type": "absolute",
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"reference_compile": true,
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"repad_logits_with_grad": false,
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"sep_token_id": 50282,
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"sparse_pred_ignore_index": -100,
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"sparse_prediction": false,
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"torch_dtype": "float32",
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"transformers_version": "4.48.0",
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"vocab_size": 50368
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d1ebd09fbfdea19c90e4c5d2b3d97c2ff2dba71f2d94f7bef3bdd2cd1906847a
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size 598436708
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special_tokens_map.json
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{
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"cls_token": {
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"content": "[CLS]",
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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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},
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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": {
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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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},
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"sep_token": {
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"content": "[SEP]",
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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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},
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"unk_token": {
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"content": "[UNK]",
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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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}
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}
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tokenizer.json
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tokenizer_config.json
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