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- README.md +0 -0
- checkpoint-300000/1_Pooling/config.json +10 -0
- checkpoint-300000/README.md +0 -0
- checkpoint-300000/config.json +45 -0
- checkpoint-300000/config_sentence_transformers.json +14 -0
- checkpoint-300000/model.safetensors +3 -0
- checkpoint-300000/modules.json +14 -0
- checkpoint-300000/optimizer.pt +3 -0
- checkpoint-300000/rng_state_0.pth +3 -0
- checkpoint-300000/rng_state_1.pth +3 -0
- checkpoint-300000/rng_state_2.pth +3 -0
- checkpoint-300000/rng_state_3.pth +3 -0
- checkpoint-300000/scaler.pt +3 -0
- checkpoint-300000/scheduler.pt +3 -0
- checkpoint-300000/sentence_bert_config.json +4 -0
- checkpoint-300000/special_tokens_map.json +37 -0
- checkpoint-300000/tokenizer.json +0 -0
- checkpoint-300000/tokenizer_config.json +952 -0
- checkpoint-300000/trainer_state.json +0 -0
- checkpoint-300000/training_args.bin +3 -0
- checkpoint-350000/1_Pooling/config.json +10 -0
- checkpoint-350000/README.md +0 -0
- checkpoint-350000/config.json +45 -0
- checkpoint-350000/config_sentence_transformers.json +14 -0
- checkpoint-350000/model.safetensors +3 -0
- checkpoint-350000/modules.json +14 -0
- checkpoint-350000/optimizer.pt +3 -0
- checkpoint-350000/rng_state_0.pth +3 -0
- checkpoint-350000/rng_state_1.pth +3 -0
- checkpoint-350000/rng_state_2.pth +3 -0
- checkpoint-350000/rng_state_3.pth +3 -0
- checkpoint-350000/scaler.pt +3 -0
- checkpoint-350000/scheduler.pt +3 -0
- checkpoint-350000/sentence_bert_config.json +4 -0
- checkpoint-350000/special_tokens_map.json +37 -0
- checkpoint-350000/tokenizer.json +0 -0
- checkpoint-350000/tokenizer_config.json +952 -0
- checkpoint-350000/trainer_state.json +0 -0
- checkpoint-350000/training_args.bin +3 -0
- checkpoint-400000/1_Pooling/config.json +10 -0
- checkpoint-400000/README.md +0 -0
- checkpoint-400000/config.json +45 -0
- checkpoint-400000/config_sentence_transformers.json +14 -0
- checkpoint-400000/model.safetensors +3 -0
- checkpoint-400000/modules.json +14 -0
- checkpoint-400000/optimizer.pt +3 -0
- checkpoint-400000/rng_state_0.pth +3 -0
- checkpoint-400000/rng_state_1.pth +3 -0
- checkpoint-400000/rng_state_2.pth +3 -0
MODEL_CARD.md
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| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
tags:
|
| 6 |
+
- sentence-transformers
|
| 7 |
+
- scientific-literature
|
| 8 |
+
- title-abstract-similarity
|
| 9 |
+
- VICReg
|
| 10 |
+
- ModernBERT
|
| 11 |
+
- biomedical
|
| 12 |
+
- research
|
| 13 |
+
pipeline_tag: feature-extraction
|
| 14 |
+
---
|
| 15 |
+
|
| 16 |
+
# SODA-VEC: Scientific Literature Embeddings with VICReg Loss
|
| 17 |
+
|
| 18 |
+
## Model Description
|
| 19 |
+
|
| 20 |
+
**SODA-VEC** is a state-of-the-art sentence transformer model specifically designed for scientific literature, trained on a massive dataset of 26+ million title-abstract pairs from PubMed Central (PMC). The model uses a custom VICReg loss configuration optimized for learning robust representations of scientific text.
|
| 21 |
+
|
| 22 |
+
### Key Features
|
| 23 |
+
|
| 24 |
+
- **Base Model**: `answerdotai/ModernBERT-base` (768-dimensional embeddings)
|
| 25 |
+
- **Sequence Length**: 512 tokens (optimized for memory efficiency)
|
| 26 |
+
- **Training Data**: 26,209,161 title-abstract pairs from PMC
|
| 27 |
+
- **Loss Configuration**: Custom VICReg loss with dot product, variance, and covariance regularization
|
| 28 |
+
- **Total Training**: 550,000 steps (~3 epochs) with stable convergence
|
| 29 |
+
|
| 30 |
+
## Model Performance
|
| 31 |
+
|
| 32 |
+
### Training Results
|
| 33 |
+
|
| 34 |
+
The model achieved excellent performance with stable training throughout:
|
| 35 |
+
|
| 36 |
+
- **Final Training Loss**: 0.8022
|
| 37 |
+
- **Final Evaluation Loss**: 0.8184
|
| 38 |
+
- **Training Duration**: 2.915 days
|
| 39 |
+
- **Total Steps**: 500,000 (resumed from step 100,000)
|
| 40 |
+
- **Evaluation Dataset**: 50% of test set (132,369 samples)
|
| 41 |
+
|
| 42 |
+
### Training Curves
|
| 43 |
+
|
| 44 |
+
The model shows excellent learning dynamics:
|
| 45 |
+
|
| 46 |
+
1. **Initial Phase (0-100k steps)**: Rapid learning from ~0.95 to ~0.85-0.90
|
| 47 |
+
2. **Mid-Phase (100k-300k steps)**: Gradual improvement to ~0.82-0.85
|
| 48 |
+
3. **Final Phase (300k-500k steps)**: Stable convergence to ~0.80-0.82
|
| 49 |
+
|
| 50 |
+
The evaluation loss shows a clear downward trend from 0.89 to 0.8184, indicating excellent generalization.
|
| 51 |
+
|
| 52 |
+
## Training History
|
| 53 |
+
|
| 54 |
+
### Phase 1: Initial Training (0-106k steps)
|
| 55 |
+
- **Configuration**: `dot_std_cov` loss (dot_loss + std_loss + cov_loss)
|
| 56 |
+
- **Learning Rate**: 2e-4
|
| 57 |
+
- **Batch Size**: 32 per device, 4 GPUs
|
| 58 |
+
- **Result**: Training became unstable with loss explosion >250 and gradient norm NaN
|
| 59 |
+
|
| 60 |
+
### Phase 2: Resumed Training (100k-550k steps)
|
| 61 |
+
- **Resume Point**: Checkpoint-100000 (stable evaluation loss)
|
| 62 |
+
- **Configuration**: Same `dot_std_cov` loss
|
| 63 |
+
- **Learning Rate**: 8e-5 (reduced for stability)
|
| 64 |
+
- **Warmup Steps**: 1,000
|
| 65 |
+
- **Max Grad Norm**: 2.0 (increased for stability)
|
| 66 |
+
- **Evaluation**: 50% of test dataset for efficiency
|
| 67 |
+
- **Result**: Stable training with excellent convergence
|
| 68 |
+
|
| 69 |
+
## Technical Specifications
|
| 70 |
+
|
| 71 |
+
### Model Architecture
|
| 72 |
+
- **Base**: ModernBERT-base (22 layers, 768 hidden size, 12 attention heads)
|
| 73 |
+
- **Pooling**: Mean pooling
|
| 74 |
+
- **Embedding Dimension**: 768
|
| 75 |
+
- **Max Sequence Length**: 512 tokens
|
| 76 |
+
- **Vocabulary Size**: 50,368 tokens
|
| 77 |
+
|
| 78 |
+
### Training Configuration
|
| 79 |
+
```python
|
| 80 |
+
{
|
| 81 |
+
"learning_rate": 8e-5,
|
| 82 |
+
"warmup_steps": 1000,
|
| 83 |
+
"max_grad_norm": 2.0,
|
| 84 |
+
"weight_decay": 0.01,
|
| 85 |
+
"batch_size": 32,
|
| 86 |
+
"gradient_accumulation_steps": 1,
|
| 87 |
+
"fp16": True,
|
| 88 |
+
"lr_scheduler_type": "cosine",
|
| 89 |
+
"max_steps": 500000,
|
| 90 |
+
"eval_steps": 10000,
|
| 91 |
+
"save_steps": 50000
|
| 92 |
+
}
|
| 93 |
+
```
|
| 94 |
+
|
| 95 |
+
### Loss Function
|
| 96 |
+
The model uses a custom VICReg loss configuration:
|
| 97 |
+
|
| 98 |
+
```python
|
| 99 |
+
loss_config = "dot_std_cov"
|
| 100 |
+
# Components:
|
| 101 |
+
# - dot_loss: Encourages high cosine similarity between paired embeddings
|
| 102 |
+
# - std_loss: Variance regularization to prevent dimensional collapse
|
| 103 |
+
# - cov_loss: Covariance regularization to decorrelate features
|
| 104 |
+
```
|
| 105 |
+
|
| 106 |
+
## Usage
|
| 107 |
+
|
| 108 |
+
### Basic Usage
|
| 109 |
+
|
| 110 |
+
```python
|
| 111 |
+
from sentence_transformers import SentenceTransformer
|
| 112 |
+
|
| 113 |
+
# Load the model
|
| 114 |
+
model = SentenceTransformer('EMBO/soda-vec-dot-std-cov-losses')
|
| 115 |
+
|
| 116 |
+
# Encode sentences
|
| 117 |
+
sentences = [
|
| 118 |
+
"Machine learning approaches for drug discovery",
|
| 119 |
+
"Deep learning methods in computational biology"
|
| 120 |
+
]
|
| 121 |
+
embeddings = model.encode(sentences)
|
| 122 |
+
|
| 123 |
+
# Compute similarity
|
| 124 |
+
similarity = model.similarity(sentences[0], sentences[1])
|
| 125 |
+
```
|
| 126 |
+
|
| 127 |
+
### Advanced Usage
|
| 128 |
+
|
| 129 |
+
```python
|
| 130 |
+
from sentence_transformers import SentenceTransformer
|
| 131 |
+
import numpy as np
|
| 132 |
+
|
| 133 |
+
model = SentenceTransformer('EMBO/soda-vec-dot-std-cov-losses')
|
| 134 |
+
|
| 135 |
+
# Encode with custom parameters
|
| 136 |
+
embeddings = model.encode(
|
| 137 |
+
sentences,
|
| 138 |
+
batch_size=32,
|
| 139 |
+
show_progress_bar=True,
|
| 140 |
+
convert_to_numpy=True
|
| 141 |
+
)
|
| 142 |
+
|
| 143 |
+
# Compute pairwise similarities
|
| 144 |
+
similarities = model.similarity(sentences, sentences)
|
| 145 |
+
```
|
| 146 |
+
|
| 147 |
+
## Training Data
|
| 148 |
+
|
| 149 |
+
### Dataset
|
| 150 |
+
- **Source**: `EMBO/soda-vec-data-full_pmc_title_abstract`
|
| 151 |
+
- **Size**: 26,209,161 training pairs, 264,739 test pairs
|
| 152 |
+
- **Content**: Title-abstract pairs from PubMed Central
|
| 153 |
+
- **Language**: English
|
| 154 |
+
- **Domain**: Scientific literature across all biomedical fields
|
| 155 |
+
|
| 156 |
+
### Data Processing
|
| 157 |
+
- **Train/Test Split**: 99% training, 1% testing
|
| 158 |
+
- **Evaluation Fraction**: 50% of test set used for evaluation
|
| 159 |
+
- **Text Processing**: Standard sentence transformer preprocessing
|
| 160 |
+
- **Tokenization**: ModernBERT tokenizer with 512 token limit
|
| 161 |
+
|
| 162 |
+
## Evaluation
|
| 163 |
+
|
| 164 |
+
The model was evaluated on a held-out test set of 132,369 samples (50% of total test set):
|
| 165 |
+
|
| 166 |
+
- **Final Evaluation Loss**: 0.8184
|
| 167 |
+
- **Training-Eval Alignment**: Excellent (0.8022 vs 0.8184)
|
| 168 |
+
- **Generalization**: Strong performance on unseen data
|
| 169 |
+
- **Stability**: No overfitting observed
|
| 170 |
+
|
| 171 |
+
## Model Files
|
| 172 |
+
|
| 173 |
+
The model includes all necessary components:
|
| 174 |
+
|
| 175 |
+
- `config.json`: Model configuration
|
| 176 |
+
- `model.safetensors`: Model weights (596MB)
|
| 177 |
+
- `tokenizer.json`: Tokenizer configuration
|
| 178 |
+
- `tokenizer_config.json`: Tokenizer settings
|
| 179 |
+
- `sentence_bert_config.json`: Sentence transformer configuration
|
| 180 |
+
- `modules.json`: Model architecture
|
| 181 |
+
- `special_tokens_map.json`: Special tokens mapping
|
| 182 |
+
|
| 183 |
+
## Citation
|
| 184 |
+
|
| 185 |
+
If you use this model in your research, please cite:
|
| 186 |
+
|
| 187 |
+
```bibtex
|
| 188 |
+
@misc{soda-vec-dot-std-cov-losses,
|
| 189 |
+
title={SODA-VEC: Scientific Literature Embeddings with VICReg Loss},
|
| 190 |
+
author={EMBO},
|
| 191 |
+
year={2024},
|
| 192 |
+
url={https://huggingface.co/EMBO/soda-vec-dot-std-cov-losses}
|
| 193 |
+
}
|
| 194 |
+
```
|
| 195 |
+
|
| 196 |
+
## License
|
| 197 |
+
|
| 198 |
+
This model is released under the MIT License. See the LICENSE file for details.
|
| 199 |
+
|
| 200 |
+
## Acknowledgments
|
| 201 |
+
|
| 202 |
+
- **Base Model**: Built on `answerdotai/ModernBERT-base`
|
| 203 |
+
- **Training Framework**: Hugging Face Transformers and Sentence Transformers
|
| 204 |
+
- **Data Source**: PubMed Central (PMC) via Hugging Face Datasets
|
| 205 |
+
- **Infrastructure**: EMBO computational resources
|
| 206 |
+
|
| 207 |
+
## Contact
|
| 208 |
+
|
| 209 |
+
For questions or issues, please contact the EMBO team or open an issue on the model repository.
|
| 210 |
+
|
| 211 |
+
---
|
| 212 |
+
|
| 213 |
+
*This model represents a significant advancement in scientific literature embeddings, combining the power of ModernBERT with carefully tuned VICReg loss for optimal representation learning.*
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README.md
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checkpoint-300000/1_Pooling/config.json
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| 1 |
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{
|
| 2 |
+
"word_embedding_dimension": 768,
|
| 3 |
+
"pooling_mode_cls_token": false,
|
| 4 |
+
"pooling_mode_mean_tokens": true,
|
| 5 |
+
"pooling_mode_max_tokens": false,
|
| 6 |
+
"pooling_mode_mean_sqrt_len_tokens": false,
|
| 7 |
+
"pooling_mode_weightedmean_tokens": false,
|
| 8 |
+
"pooling_mode_lasttoken": false,
|
| 9 |
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"include_prompt": true
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| 10 |
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}
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checkpoint-300000/README.md
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checkpoint-300000/config.json
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{
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| 2 |
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"architectures": [
|
| 3 |
+
"ModernBertModel"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 50281,
|
| 8 |
+
"classifier_activation": "gelu",
|
| 9 |
+
"classifier_bias": false,
|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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"global_rope_theta": 160000.0,
|
| 19 |
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|
| 20 |
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"hidden_activation": "gelu",
|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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"layer_norm_eps": 1e-05,
|
| 26 |
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|
| 27 |
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"local_rope_theta": 10000.0,
|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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"model_type": "modernbert",
|
| 32 |
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"norm_bias": false,
|
| 33 |
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|
| 34 |
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"num_attention_heads": 12,
|
| 35 |
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|
| 36 |
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|
| 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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"vocab_size": 50368
|
| 45 |
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}
|
checkpoint-300000/config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,14 @@
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|
| 1 |
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{
|
| 2 |
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"model_type": "SentenceTransformer",
|
| 3 |
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"__version__": {
|
| 4 |
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"sentence_transformers": "5.0.0",
|
| 5 |
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"transformers": "4.54.1",
|
| 6 |
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"pytorch": "2.7.1+cu126"
|
| 7 |
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},
|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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"default_prompt_name": null,
|
| 13 |
+
"similarity_fn_name": "cosine"
|
| 14 |
+
}
|
checkpoint-300000/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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| 3 |
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size 596070136
|
checkpoint-300000/modules.json
ADDED
|
@@ -0,0 +1,14 @@
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| 3 |
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"idx": 0,
|
| 4 |
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"name": "0",
|
| 5 |
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|
| 6 |
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"type": "sentence_transformers.models.Transformer"
|
| 7 |
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|
| 8 |
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{
|
| 9 |
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"idx": 1,
|
| 10 |
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"name": "1",
|
| 11 |
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"path": "1_Pooling",
|
| 12 |
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"type": "sentence_transformers.models.Pooling"
|
| 13 |
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|
| 14 |
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|
checkpoint-300000/optimizer.pt
ADDED
|
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size 1192227531
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checkpoint-300000/rng_state_0.pth
ADDED
|
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checkpoint-300000/rng_state_1.pth
ADDED
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checkpoint-300000/rng_state_2.pth
ADDED
|
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size 15365
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checkpoint-300000/rng_state_3.pth
ADDED
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version https://git-lfs.github.com/spec/v1
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size 15365
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checkpoint-300000/scaler.pt
ADDED
|
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size 1383
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checkpoint-300000/scheduler.pt
ADDED
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version https://git-lfs.github.com/spec/v1
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size 1465
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checkpoint-300000/sentence_bert_config.json
ADDED
|
@@ -0,0 +1,4 @@
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| 1 |
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{
|
| 2 |
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|
| 3 |
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"do_lower_case": false
|
| 4 |
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}
|
checkpoint-300000/special_tokens_map.json
ADDED
|
@@ -0,0 +1,37 @@
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|
| 1 |
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{
|
| 2 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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"normalized": false,
|
| 6 |
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|
| 7 |
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|
| 8 |
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},
|
| 9 |
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"mask_token": {
|
| 10 |
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"content": "[MASK]",
|
| 11 |
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"lstrip": true,
|
| 12 |
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"normalized": false,
|
| 13 |
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"rstrip": false,
|
| 14 |
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"single_word": false
|
| 15 |
+
},
|
| 16 |
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"pad_token": {
|
| 17 |
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"content": "[PAD]",
|
| 18 |
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"lstrip": false,
|
| 19 |
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"normalized": false,
|
| 20 |
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"rstrip": false,
|
| 21 |
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"single_word": false
|
| 22 |
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|
| 23 |
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"sep_token": {
|
| 24 |
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"content": "[SEP]",
|
| 25 |
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|
| 26 |
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"normalized": false,
|
| 27 |
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"rstrip": false,
|
| 28 |
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"single_word": false
|
| 29 |
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},
|
| 30 |
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"unk_token": {
|
| 31 |
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"content": "[UNK]",
|
| 32 |
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"lstrip": false,
|
| 33 |
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"normalized": false,
|
| 34 |
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"rstrip": false,
|
| 35 |
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"single_word": false
|
| 36 |
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}
|
| 37 |
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}
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checkpoint-300000/tokenizer.json
ADDED
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The diff for this file is too large to render.
See raw diff
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|
|
checkpoint-300000/tokenizer_config.json
ADDED
|
@@ -0,0 +1,952 @@
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| 1 |
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| 2 |
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checkpoint-300000/trainer_state.json
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checkpoint-300000/training_args.bin
ADDED
|
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size 6353
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checkpoint-350000/1_Pooling/config.json
ADDED
|
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|
| 1 |
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|
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|
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|
| 4 |
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|
| 5 |
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|
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|
| 7 |
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|
| 8 |
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|
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|
| 10 |
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|
checkpoint-350000/README.md
ADDED
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|
|
checkpoint-350000/config.json
ADDED
|
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|
|
|
|
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|
|
|
|
|
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|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
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|
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|
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|
|
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|
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|
|
| 1 |
+
{
|
| 2 |
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"architectures": [
|
| 3 |
+
"ModernBertModel"
|
| 4 |
+
],
|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
+
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|
| 20 |
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|
| 21 |
+
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|
| 22 |
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|
| 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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|
| 28 |
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|
| 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 |
+
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|
| 35 |
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|
| 36 |
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|
| 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 |
+
"vocab_size": 50368
|
| 45 |
+
}
|
checkpoint-350000/config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
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|
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|
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|
| 4 |
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|
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|
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|
| 14 |
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|
checkpoint-350000/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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| 3 |
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size 596070136
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checkpoint-350000/modules.json
ADDED
|
@@ -0,0 +1,14 @@
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[
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|
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|
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|
| 6 |
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|
| 7 |
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|
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|
| 9 |
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|
| 10 |
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"name": "1",
|
| 11 |
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"path": "1_Pooling",
|
| 12 |
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"type": "sentence_transformers.models.Pooling"
|
| 13 |
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|
| 14 |
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|
checkpoint-350000/optimizer.pt
ADDED
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checkpoint-350000/rng_state_0.pth
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checkpoint-350000/rng_state_1.pth
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checkpoint-350000/rng_state_2.pth
ADDED
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checkpoint-350000/rng_state_3.pth
ADDED
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size 15365
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checkpoint-350000/scaler.pt
ADDED
|
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version https://git-lfs.github.com/spec/v1
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size 1383
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checkpoint-350000/scheduler.pt
ADDED
|
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| 3 |
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size 1465
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checkpoint-350000/sentence_bert_config.json
ADDED
|
@@ -0,0 +1,4 @@
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|
| 1 |
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{
|
| 2 |
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|
| 3 |
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|
| 4 |
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|
checkpoint-350000/special_tokens_map.json
ADDED
|
@@ -0,0 +1,37 @@
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| 1 |
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| 2 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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"content": "[PAD]",
|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 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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|
| 28 |
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|
| 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 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
checkpoint-350000/tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
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|
|
checkpoint-350000/tokenizer_config.json
ADDED
|
@@ -0,0 +1,952 @@
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checkpoint-350000/trainer_state.json
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checkpoint-350000/training_args.bin
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checkpoint-400000/1_Pooling/config.json
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checkpoint-400000/README.md
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checkpoint-400000/config.json
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checkpoint-400000/config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,14 @@
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|
| 4 |
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|
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|
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|
| 7 |
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|
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|
| 13 |
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|
| 14 |
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checkpoint-400000/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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size 596070136
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checkpoint-400000/modules.json
ADDED
|
@@ -0,0 +1,14 @@
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| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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+
]
|
checkpoint-400000/optimizer.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:870cfbcf40a31e8a10bbd0698d3c2d6cf1f0da09b0a1674dc2a6d8064760441f
|
| 3 |
+
size 1192227531
|
checkpoint-400000/rng_state_0.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:da0c8c3e470082ac48e6f815e16aeaceab09c84fc50eac00d9ea35690d035ec1
|
| 3 |
+
size 15365
|
checkpoint-400000/rng_state_1.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:578bc70684a0ceec85b750498245096b3b2712e1572830b39cf721dba97888c1
|
| 3 |
+
size 15365
|
checkpoint-400000/rng_state_2.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:21e15f2dba30d8e3a6c6642d8d1c95c7e90939a353aed47e921178c413a5feee
|
| 3 |
+
size 15365
|