Add model
Browse files- README.md +96 -0
- open_clip_config.json +46 -0
- open_clip_model.safetensors +3 -0
- open_clip_pytorch_model.bin +3 -0
- special_tokens_map.json +107 -0
- tokenizer.json +0 -0
- tokenizer_config.json +939 -0
README.md
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| 1 |
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---
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tags:
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- clip
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- siglip
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library_name: open_clip
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pipeline_tag: zero-shot-image-classification
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license: apache-2.0
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datasets:
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- webli
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---
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# Model card for ViT-SO400M-14-SigLIP
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A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI.
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This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only).
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## Model Details
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- **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification.
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- **Original:** https://github.com/google-research/big_vision
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- **Dataset:** WebLI
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- **Papers:**
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- Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343
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## Model Usage
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### With OpenCLIP
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```
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import torch
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import torch.nn.functional as F
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from urllib.request import urlopen
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from PIL import Image
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from open_clip import create_model_from_pretrained, get_tokenizer
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model, preprocess = create_model_from_pretrained('hf-hub:ViT-SO400M-14-SigLIP')
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tokenizer = get_tokenizer('hf-hub:ViT-SO400M-14-SigLIP')
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image = Image.open(urlopen(
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
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))
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image = preprocess(image).unsqueeze(0)
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text = tokenizer(["a diagram", "a dog", "a cat", "a beignet"], context_length=model.context_length)
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with torch.no_grad(), torch.cuda.amp.autocast():
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image_features = model.encode_image(image)
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text_features = model.encode_text(text)
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image_features = F.normalize(image_features, dim=-1)
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text_features = F.normalize(text_features, dim=-1)
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text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
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print("Label probs:", text_probs) # prints: [[0., 0., 0., 1.0]]
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```
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### With `timm` (for image embeddings)
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```python
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from urllib.request import urlopen
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from PIL import Image
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import timm
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image = Image.open(urlopen(
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
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))
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model = timm.create_model(
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'vit_so400m_patch14_siglip_224',
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pretrained=True,
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num_classes=0,
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)
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model = model.eval()
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# get model specific transforms (normalization, resize)
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data_config = timm.data.resolve_model_data_config(model)
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transforms = timm.data.create_transform(**data_config, is_training=False)
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output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
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```
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## Citation
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```bibtex
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@article{zhai2023sigmoid,
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title={Sigmoid loss for language image pre-training},
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author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
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journal={arXiv preprint arXiv:2303.15343},
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year={2023}
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}
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```
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```bibtex
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@misc{big_vision,
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author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
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title = {Big Vision},
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year = {2022},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/google-research/big_vision}}
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}
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```
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open_clip_config.json
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{
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"model_cfg": {
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"embed_dim": 1152,
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"init_logit_bias": -10,
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"custom_text": true,
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"vision_cfg": {
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"image_size": 224,
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"timm_model_name": "vit_so400m_patch14_siglip_224",
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"timm_model_pretrained": false,
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"timm_pool": "map",
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"timm_proj": "none"
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},
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"text_cfg": {
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"context_length": 16,
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"vocab_size": 32000,
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"tokenizer_kwargs": {
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"clean": "canonicalize"
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},
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"width": 1152,
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"heads": 16,
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"layers": 27,
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"mlp_ratio": 3.7362,
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"no_causal_mask": true,
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"proj_bias": true,
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"pool_type": "last",
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"norm_kwargs": {
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"eps": 1e-06
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},
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"hf_tokenizer_name": "timm/ViT-SO400M-14-SigLIP"
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}
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},
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"preprocess_cfg": {
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"mean": [
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0.5,
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0.5,
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0.5
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],
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"std": [
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0.5,
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0.5,
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0.5
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],
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"interpolation": "bicubic",
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"resize_mode": "squash"
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}
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}
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open_clip_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0a04a48b797e187a568335ab67e57a8958c32cee5a707a3feb6d4c97149dcd9c
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size 3509517656
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open_clip_pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:19d5c4b9a50a301af4de8e51383abf4c8b0315196969efd646a3e51c1f929ffd
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size 3509715022
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special_tokens_map.json
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{
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"additional_special_tokens": [
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],
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"eos_token": "</s>",
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"pad_token": "</s>",
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"unk_token": "<unk>"
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}
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tokenizer.json
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tokenizer_config.json
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|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "<pad>",
|
| 5 |
+
"lstrip": true,
|
| 6 |
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|
| 7 |
+
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|
| 8 |
+
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|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
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"1": {
|
| 12 |
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"content": "</s>",
|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
+
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|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
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"2": {
|
| 20 |
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"content": "<unk>",
|
| 21 |
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|
| 22 |
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"normalized": true,
|
| 23 |
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|
| 24 |
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|
| 25 |
+
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|
| 26 |
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},
|
| 27 |
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"32000": {
|
| 28 |
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"content": "<extra_id_99>",
|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
+
},
|
| 35 |
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|
| 36 |
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"content": "<extra_id_98>",
|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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},
|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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| 47 |
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| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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| 54 |
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| 55 |
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| 56 |
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| 57 |
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|
| 58 |
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| 59 |
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|
| 60 |
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"content": "<extra_id_95>",
|
| 61 |
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| 62 |
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| 63 |
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| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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"content": "<extra_id_94>",
|
| 69 |
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|
| 70 |
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| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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| 79 |
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| 80 |
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|
| 81 |
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|
| 82 |
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| 83 |
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|
| 84 |
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| 85 |
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| 86 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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|
| 91 |
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|
| 92 |
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| 93 |
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| 94 |
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| 95 |
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| 96 |
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| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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|
| 101 |
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| 102 |
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| 103 |
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| 104 |
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|
| 105 |
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|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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| 114 |
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| 115 |
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| 116 |
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| 117 |
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| 118 |
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| 119 |
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| 120 |
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| 121 |
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| 122 |
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| 123 |
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|
| 124 |
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| 125 |
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| 126 |
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| 127 |
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| 128 |
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|
| 129 |
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| 130 |
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| 131 |
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| 132 |
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|
| 133 |
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| 134 |
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| 135 |
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| 136 |
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| 137 |
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| 138 |
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| 139 |
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|
| 140 |
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| 141 |
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| 142 |
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| 143 |
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| 144 |
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| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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| 150 |
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| 151 |
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| 152 |
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|
| 153 |
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|
| 154 |
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| 155 |
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|
| 156 |
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|
| 157 |
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| 158 |
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| 159 |
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| 160 |
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|
| 161 |
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|
| 162 |
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| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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| 167 |
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| 168 |
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| 169 |
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|
| 170 |
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|
| 171 |
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|
| 172 |
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|
| 173 |
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| 174 |
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| 175 |
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| 176 |
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|
| 177 |
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|
| 178 |
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| 179 |
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|
| 180 |
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|
| 181 |
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| 182 |
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| 183 |
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| 184 |
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| 185 |
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|
| 186 |
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| 187 |
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|
| 188 |
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|
| 189 |
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|
| 190 |
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|
| 191 |
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|
| 192 |
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|
| 193 |
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|
| 194 |
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|
| 195 |
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|
| 196 |
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|
| 197 |
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| 198 |
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| 199 |
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| 200 |
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|
| 201 |
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|
| 202 |
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|
| 203 |
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|
| 204 |
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|
| 205 |
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|
| 206 |
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| 207 |
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| 208 |
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|
| 209 |
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|
| 210 |
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|
| 211 |
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|
| 212 |
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|
| 213 |
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|
| 214 |
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| 215 |
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| 216 |
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|
| 217 |
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|
| 218 |
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|
| 219 |
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|
| 220 |
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|
| 221 |
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|
| 222 |
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| 223 |
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| 224 |
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| 225 |
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|
| 226 |
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| 227 |
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|
| 228 |
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|
| 229 |
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| 230 |
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| 231 |
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| 232 |
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|
| 233 |
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|
| 234 |
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|
| 235 |
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|
| 236 |
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|
| 237 |
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|
| 238 |
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| 239 |
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| 240 |
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|
| 241 |
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|
| 242 |
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|
| 243 |
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|
| 244 |
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|
| 245 |
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| 246 |
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| 247 |
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| 248 |
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|
| 249 |
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|
| 250 |
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|
| 251 |
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|
| 252 |
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|
| 253 |
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|
| 254 |
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| 255 |
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|
| 256 |
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|
| 257 |
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|
| 258 |
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|
| 259 |
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|
| 260 |
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|
| 261 |
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|
| 262 |
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| 263 |
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| 264 |
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| 265 |
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|
| 266 |
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|
| 267 |
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|
| 268 |
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|
| 269 |
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|
| 270 |
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| 271 |
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| 272 |
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|
| 273 |
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|
| 274 |
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| 275 |
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|
| 276 |
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|
| 277 |
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|
| 278 |
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|
| 279 |
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|
| 280 |
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|
| 281 |
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|
| 282 |
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|
| 283 |
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|
| 284 |
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|
| 285 |
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|
| 286 |
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|
| 287 |
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|
| 288 |
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|
| 289 |
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|
| 290 |
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|
| 291 |
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|
| 292 |
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|
| 293 |
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|
| 294 |
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|
| 295 |
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|
| 296 |
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|
| 297 |
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|
| 298 |
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|
| 299 |
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|
| 300 |
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|
| 301 |
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|
| 302 |
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|
| 303 |
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|
| 304 |
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|
| 305 |
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|
| 306 |
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|
| 307 |
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|
| 308 |
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|
| 309 |
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|
| 310 |
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|
| 311 |
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|
| 312 |
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|
| 313 |
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|
| 314 |
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|
| 315 |
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|
| 316 |
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|
| 317 |
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|
| 318 |
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|
| 319 |
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|
| 320 |
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|
| 321 |
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|
| 322 |
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|
| 323 |
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|
| 324 |
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|
| 325 |
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|
| 326 |
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| 327 |
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| 328 |
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|
| 329 |
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|
| 330 |
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|
| 331 |
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|
| 332 |
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|
| 333 |
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|
| 334 |
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| 335 |
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|
| 336 |
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|
| 337 |
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|
| 338 |
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|
| 339 |
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|
| 340 |
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|
| 341 |
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|
| 342 |
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|
| 343 |
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|
| 344 |
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|
| 345 |
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|
| 346 |
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|
| 347 |
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|
| 348 |
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|
| 349 |
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|
| 350 |
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| 351 |
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| 352 |
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| 353 |
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| 354 |
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| 355 |
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|
| 356 |
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| 357 |
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| 358 |
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| 359 |
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| 360 |
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| 361 |
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| 362 |
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| 363 |
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|
| 364 |
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|
| 365 |
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| 366 |
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| 367 |
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| 368 |
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|
| 369 |
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|
| 370 |
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|
| 371 |
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|
| 372 |
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|
| 373 |
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|
| 374 |
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|
| 375 |
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| 376 |
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|
| 377 |
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|
| 378 |
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|
| 379 |
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|
| 380 |
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|
| 381 |
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| 382 |
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| 383 |
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|
| 384 |
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|
| 385 |
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| 386 |
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|
| 387 |
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|
| 388 |
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|
| 389 |
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|
| 390 |
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|
| 391 |
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|
| 392 |
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|
| 393 |
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| 394 |
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|
| 395 |
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| 396 |
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|
| 397 |
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| 398 |
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| 399 |
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| 400 |
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| 401 |
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| 402 |
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|
| 403 |
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| 404 |
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|
| 405 |
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| 406 |
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| 407 |
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| 408 |
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| 409 |
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|
| 410 |
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|
| 411 |
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| 412 |
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|
| 413 |
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| 414 |
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| 415 |
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| 416 |
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|
| 417 |
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| 418 |
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|
| 419 |
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| 420 |
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| 421 |
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| 422 |
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| 423 |
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| 424 |
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| 425 |
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| 426 |
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| 427 |
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| 428 |
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|
| 429 |
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"<extra_id_25>",
|
| 855 |
+
"<extra_id_26>",
|
| 856 |
+
"<extra_id_27>",
|
| 857 |
+
"<extra_id_28>",
|
| 858 |
+
"<extra_id_29>",
|
| 859 |
+
"<extra_id_30>",
|
| 860 |
+
"<extra_id_31>",
|
| 861 |
+
"<extra_id_32>",
|
| 862 |
+
"<extra_id_33>",
|
| 863 |
+
"<extra_id_34>",
|
| 864 |
+
"<extra_id_35>",
|
| 865 |
+
"<extra_id_36>",
|
| 866 |
+
"<extra_id_37>",
|
| 867 |
+
"<extra_id_38>",
|
| 868 |
+
"<extra_id_39>",
|
| 869 |
+
"<extra_id_40>",
|
| 870 |
+
"<extra_id_41>",
|
| 871 |
+
"<extra_id_42>",
|
| 872 |
+
"<extra_id_43>",
|
| 873 |
+
"<extra_id_44>",
|
| 874 |
+
"<extra_id_45>",
|
| 875 |
+
"<extra_id_46>",
|
| 876 |
+
"<extra_id_47>",
|
| 877 |
+
"<extra_id_48>",
|
| 878 |
+
"<extra_id_49>",
|
| 879 |
+
"<extra_id_50>",
|
| 880 |
+
"<extra_id_51>",
|
| 881 |
+
"<extra_id_52>",
|
| 882 |
+
"<extra_id_53>",
|
| 883 |
+
"<extra_id_54>",
|
| 884 |
+
"<extra_id_55>",
|
| 885 |
+
"<extra_id_56>",
|
| 886 |
+
"<extra_id_57>",
|
| 887 |
+
"<extra_id_58>",
|
| 888 |
+
"<extra_id_59>",
|
| 889 |
+
"<extra_id_60>",
|
| 890 |
+
"<extra_id_61>",
|
| 891 |
+
"<extra_id_62>",
|
| 892 |
+
"<extra_id_63>",
|
| 893 |
+
"<extra_id_64>",
|
| 894 |
+
"<extra_id_65>",
|
| 895 |
+
"<extra_id_66>",
|
| 896 |
+
"<extra_id_67>",
|
| 897 |
+
"<extra_id_68>",
|
| 898 |
+
"<extra_id_69>",
|
| 899 |
+
"<extra_id_70>",
|
| 900 |
+
"<extra_id_71>",
|
| 901 |
+
"<extra_id_72>",
|
| 902 |
+
"<extra_id_73>",
|
| 903 |
+
"<extra_id_74>",
|
| 904 |
+
"<extra_id_75>",
|
| 905 |
+
"<extra_id_76>",
|
| 906 |
+
"<extra_id_77>",
|
| 907 |
+
"<extra_id_78>",
|
| 908 |
+
"<extra_id_79>",
|
| 909 |
+
"<extra_id_80>",
|
| 910 |
+
"<extra_id_81>",
|
| 911 |
+
"<extra_id_82>",
|
| 912 |
+
"<extra_id_83>",
|
| 913 |
+
"<extra_id_84>",
|
| 914 |
+
"<extra_id_85>",
|
| 915 |
+
"<extra_id_86>",
|
| 916 |
+
"<extra_id_87>",
|
| 917 |
+
"<extra_id_88>",
|
| 918 |
+
"<extra_id_89>",
|
| 919 |
+
"<extra_id_90>",
|
| 920 |
+
"<extra_id_91>",
|
| 921 |
+
"<extra_id_92>",
|
| 922 |
+
"<extra_id_93>",
|
| 923 |
+
"<extra_id_94>",
|
| 924 |
+
"<extra_id_95>",
|
| 925 |
+
"<extra_id_96>",
|
| 926 |
+
"<extra_id_97>",
|
| 927 |
+
"<extra_id_98>",
|
| 928 |
+
"<extra_id_99>"
|
| 929 |
+
],
|
| 930 |
+
"clean_up_tokenization_spaces": true,
|
| 931 |
+
"eos_token": "</s>",
|
| 932 |
+
"extra_ids": 100,
|
| 933 |
+
"legacy": false,
|
| 934 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 935 |
+
"pad_token": "</s>",
|
| 936 |
+
"sp_model_kwargs": {},
|
| 937 |
+
"tokenizer_class": "T5Tokenizer",
|
| 938 |
+
"unk_token": "<unk>"
|
| 939 |
+
}
|