Praise2112
commited on
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +85 -0
- all_results.json +19 -0
- config.json +50 -0
- eval_nbest_predictions.json +3 -0
- eval_null_odds.json +0 -0
- eval_predictions.json +0 -0
- eval_results.json +19 -0
- model.safetensors +3 -0
- modelling.py +139 -0
- modernBERT-large.yaml +33 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +945 -0
- training_args.bin +3 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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eval_nbest_predictions.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: answerdotai/ModernBERT-large
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tags:
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- generated_from_trainer
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datasets:
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- rajpurkar/squad_v2
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model-index:
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- name: ModernBERT-large-squad2-v0.1
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# ModernBERT-large-squad2-v0.1
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This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on the rajpurkar/squad_v2 dataset.
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Maximum sequence length used during training was 8192.
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Requires `trust_remote_code` to be set to `True` in order to be load the model.
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```python
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from transformers import pipeline
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model_name = "praise2112/ModernBERT-large-squad2-v0.1"
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# a) Get predictions
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
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context = """Model Summary
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ModernBERT is a modernized bidirectional encoder-only Transformer model (BERT-style) pre-trained on 2 trillion tokens of English and code data with a native context length of up to 8,192 tokens. ModernBERT leverages recent architectural improvements such as:
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Rotary Positional Embeddings (RoPE) for long-context support.
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Local-Global Alternating Attention for efficiency on long inputs.
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Unpadding and Flash Attention for efficient inference.
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ModernBERT鈥檚 native long context length makes it ideal for tasks that require processing long documents, such as retrieval, classification, and semantic search within large corpora. The model was trained on a large corpus of text and code, making it suitable for a wide range of downstream tasks, including code retrieval and hybrid (text + code) semantic search.
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It is available in the following sizes:
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ModernBERT-base - 22 layers, 149 million parameters
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ModernBERT-large - 28 layers, 395 million parameters
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For more information about ModernBERT, we recommend our release blog post for a high-level overview, and our arXiv pre-print for in-depth information.
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ModernBERT is a collaboration between Answer.AI, LightOn, and friends."""
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question = "Why was RoPE used in ModernBERT?"
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res = nlp(question=question, context=context, max_seq_len=8192)
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# {'score': 0.5530015826225281, 'start': 309, 'end': 334, 'answer': ' for long-context support'}
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```
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 64
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- optimizer: Use ExtendedOptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 4
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### Training results
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| Metric | Value |
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|--------|--------|
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| eval_exact | 86.27 |
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| eval_f1 | 89.30 |
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### Framework versions
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- Transformers 4.48.0.dev0
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- Pytorch 2.5.1+cu124
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- Datasets 2.20.0
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- Tokenizers 0.21.0
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all_results.json
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{
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"epoch": 3.731246163290362,
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"eval_HasAns_exact": 81.00539811066128,
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"eval_HasAns_f1": 87.06521729339727,
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+
"eval_HasAns_total": 5928,
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"eval_NoAns_exact": 91.52228763666947,
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"eval_NoAns_f1": 91.52228763666947,
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"eval_NoAns_total": 5945,
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"eval_best_exact": 86.27137202055083,
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"eval_best_exact_thresh": 0.0,
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"eval_best_f1": 89.29694332647645,
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"eval_best_f1_thresh": 0.0,
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"eval_exact": 86.27137202055083,
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"eval_f1": 89.29694332647682,
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"eval_runtime": 64.0292,
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"eval_samples_per_second": 185.431,
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"eval_steps_per_second": 23.193,
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"eval_total": 11873
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}
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config.json
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{
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"_name_or_path": "/home/praise/PycharmProjects/encoder_playground/ModernBERT-large",
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"architectures": [
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"ModernBertForQuestionAnswering"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoModelForQuestionAnswering": "modelling.ModernBertForQuestionAnswering"
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},
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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": 0.0,
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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": 1024,
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+
"initializer_cutoff_factor": 2.0,
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+
"initializer_range": 0.02,
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+
"intermediate_size": 2624,
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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": 16,
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"num_hidden_layers": 28,
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+
"pad_token_id": 50283,
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"position_embedding_type": "absolute",
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"reference_compile": false,
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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.dev0",
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"vocab_size": 50368
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}
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eval_nbest_predictions.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:b51a86270e0b091bed40f78a08bec2101a8fe862fc8fd3c0b7e51f79af91e0f5
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size 50704854
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eval_null_odds.json
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eval_predictions.json
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eval_results.json
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{
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"epoch": 3.731246163290362,
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3 |
+
"eval_HasAns_exact": 81.00539811066128,
|
4 |
+
"eval_HasAns_f1": 87.06521729339727,
|
5 |
+
"eval_HasAns_total": 5928,
|
6 |
+
"eval_NoAns_exact": 91.52228763666947,
|
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+
"eval_NoAns_f1": 91.52228763666947,
|
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+
"eval_NoAns_total": 5945,
|
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+
"eval_best_exact": 86.27137202055083,
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+
"eval_best_exact_thresh": 0.0,
|
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+
"eval_best_f1": 89.29694332647645,
|
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+
"eval_best_f1_thresh": 0.0,
|
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+
"eval_exact": 86.27137202055083,
|
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+
"eval_f1": 89.29694332647682,
|
15 |
+
"eval_runtime": 64.0292,
|
16 |
+
"eval_samples_per_second": 185.431,
|
17 |
+
"eval_steps_per_second": 23.193,
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"eval_total": 11873
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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:d1e48f8594877e6e28fd80d164d8754fa77aa14edfa3975a6eb1581880c2e036
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size 1583351664
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modelling.py
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from typing import Optional, Tuple, Union
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import torch
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from transformers import ModernBertModel, ModernBertPreTrainedModel, ModernBertConfig
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from transformers.modeling_outputs import QuestionAnsweringModelOutput
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from transformers.models.modernbert.modeling_modernbert import _pad_modernbert_output, _unpad_modernbert_input, \
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ModernBertPredictionHead
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class ModernBertForQuestionAnswering(ModernBertPreTrainedModel):
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def __init__(self, config: ModernBertConfig):
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super().__init__(config)
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self.num_labels = config.num_labels
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self.config = config
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self.model = ModernBertModel(config)
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self.head = ModernBertPredictionHead(config)
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self.qa_outputs = nn.Linear(config.hidden_size, 2) # 2 for start/end position logits
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self.qa_outputs.weight.data.normal_(mean=0.0, std=0.02)
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self.qa_outputs.bias.data.zero_()
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self.drop = torch.nn.Dropout(config.classifier_dropout)
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# Initialize weights and apply final processing
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self.post_init()
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@torch.compile(dynamic=True)
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def compiled_head(self, output: torch.Tensor) -> torch.Tensor:
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return self.head(output)
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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attention_mask: Optional[torch.Tensor] = None,
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sliding_window_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.Tensor] = None,
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start_positions: Optional[torch.Tensor] = None,
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end_positions: Optional[torch.Tensor] = None,
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indices: Optional[torch.Tensor] = None,
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cu_seqlens: Optional[torch.Tensor] = None,
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max_seqlen: Optional[int] = None,
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batch_size: Optional[int] = None,
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seq_len: Optional[int] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
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r"""
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start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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Labels for position (index) of the start of the labelled span for computing the token classification loss.
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Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
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are not taken into account for computing the loss.
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end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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Labels for position (index) of the end of the labelled span for computing the token classification loss.
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Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
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are not taken into account for computing the loss.
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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self._maybe_set_compile()
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# Get sequence length and batch size if not provided
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# if batch_size is None or seq_len is None:
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+
# batch_size, seq_len = input_ids.shape[:2]
|
66 |
+
|
67 |
+
# # Handle Flash Attention 2 unpadding
|
68 |
+
# if self.config._attn_implementation == "flash_attention_2":
|
69 |
+
# if indices is None and cu_seqlens is None and max_seqlen is None:
|
70 |
+
# if attention_mask is None:
|
71 |
+
# attention_mask = torch.ones((batch_size, seq_len), device=input_ids.device, dtype=torch.bool)
|
72 |
+
# with torch.no_grad():
|
73 |
+
# input_ids, indices, cu_seqlens, max_seqlen, position_ids, _ = _unpad_modernbert_input(
|
74 |
+
# inputs=input_ids, attention_mask=attention_mask, position_ids=position_ids
|
75 |
+
# )
|
76 |
+
|
77 |
+
outputs = self.model(
|
78 |
+
input_ids,
|
79 |
+
attention_mask=attention_mask,
|
80 |
+
sliding_window_mask=sliding_window_mask,
|
81 |
+
position_ids=position_ids,
|
82 |
+
indices=indices,
|
83 |
+
cu_seqlens=cu_seqlens,
|
84 |
+
max_seqlen=max_seqlen,
|
85 |
+
batch_size=batch_size,
|
86 |
+
seq_len=seq_len,
|
87 |
+
output_attentions=output_attentions,
|
88 |
+
output_hidden_states=output_hidden_states,
|
89 |
+
return_dict=True,
|
90 |
+
)
|
91 |
+
|
92 |
+
sequence_output = outputs[0]
|
93 |
+
sequence_output = (
|
94 |
+
self.drop(self.compiled_head(sequence_output))
|
95 |
+
if self.config.reference_compile
|
96 |
+
else self.drop(self.head(sequence_output))
|
97 |
+
)
|
98 |
+
# sequence_output = self.drop(self.head(sequence_output))
|
99 |
+
|
100 |
+
logits = self.qa_outputs(sequence_output)
|
101 |
+
start_logits, end_logits = logits.split(1, dim=-1)
|
102 |
+
start_logits = start_logits.squeeze(-1)
|
103 |
+
end_logits = end_logits.squeeze(-1)
|
104 |
+
|
105 |
+
# # Handle Flash Attention 2 padding
|
106 |
+
# if self.config._attn_implementation == "flash_attention_2":
|
107 |
+
# start_logits = _pad_modernbert_output(inputs=start_logits, indices=indices, batch=batch_size,
|
108 |
+
# seqlen=seq_len)
|
109 |
+
# end_logits = _pad_modernbert_output(inputs=end_logits, indices=indices, batch=batch_size,
|
110 |
+
# seqlen=seq_len)
|
111 |
+
|
112 |
+
total_loss = None
|
113 |
+
if start_positions is not None and end_positions is not None:
|
114 |
+
# If we are on multi-GPU, split add a dimension
|
115 |
+
if len(start_positions.size()) > 1:
|
116 |
+
start_positions = start_positions.squeeze(-1)
|
117 |
+
if len(end_positions.size()) > 1:
|
118 |
+
end_positions = end_positions.squeeze(-1)
|
119 |
+
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
120 |
+
ignored_index = start_logits.size(1)
|
121 |
+
start_positions = start_positions.clamp(0, ignored_index)
|
122 |
+
end_positions = end_positions.clamp(0, ignored_index)
|
123 |
+
|
124 |
+
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
125 |
+
start_loss = loss_fct(start_logits, start_positions)
|
126 |
+
end_loss = loss_fct(end_logits, end_positions)
|
127 |
+
total_loss = (start_loss + end_loss) / 2
|
128 |
+
|
129 |
+
if not return_dict:
|
130 |
+
output = (start_logits, end_logits) + outputs[2:]
|
131 |
+
return ((total_loss,) + output) if total_loss is not None else output
|
132 |
+
|
133 |
+
return QuestionAnsweringModelOutput(
|
134 |
+
loss=total_loss,
|
135 |
+
start_logits=start_logits,
|
136 |
+
end_logits=end_logits,
|
137 |
+
hidden_states=outputs.hidden_states,
|
138 |
+
attentions=outputs.attentions,
|
139 |
+
)
|
modernBERT-large.yaml
ADDED
@@ -0,0 +1,33 @@
|
|
|
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|
|
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|
|
|
|
|
|
|
1 |
+
# yaml-language-server: $schema=../../config_schema.json
|
2 |
+
|
3 |
+
task: question-answering
|
4 |
+
base_model: /home/praise/PycharmProjects/encoder_playground/ModernBERT-large
|
5 |
+
|
6 |
+
data:
|
7 |
+
path: rajpurkar/squad_v2
|
8 |
+
train_split: train # this must be either train.csv or train.json
|
9 |
+
test_split: validation # this must be either valid.csv or valid.json
|
10 |
+
|
11 |
+
trainer:
|
12 |
+
trainer_name: "hf_trainer"
|
13 |
+
hf_trainer_args:
|
14 |
+
eval_steps: 200
|
15 |
+
save_steps: 200
|
16 |
+
logging_steps: 200
|
17 |
+
optim: adamw_torch
|
18 |
+
num_train_epochs: 4
|
19 |
+
learning_rate: 1.0E-5
|
20 |
+
metric_for_best_model: "f1"
|
21 |
+
greater_is_better: true
|
22 |
+
per_device_train_batch_size: 8
|
23 |
+
per_device_eval_batch_size: 8
|
24 |
+
output_dir: /home/praise/PycharmProjects/encoder_playground/runs/ModernBERT-large-squad2-v0.3
|
25 |
+
bf16: true
|
26 |
+
warmup_ratio: 0.1
|
27 |
+
gradient_accumulation_steps: 8
|
28 |
+
|
29 |
+
extra_trainer_args:
|
30 |
+
early_stopping_patience: 20
|
31 |
+
early_stopping_threshold: 0.001
|
32 |
+
|
33 |
+
huggingface_hub_username: "Praise2112"
|
special_tokens_map.json
ADDED
@@ -0,0 +1,37 @@
|
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|
|
|
1 |
+
{
|
2 |
+
"cls_token": {
|
3 |
+
"content": "[CLS]",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"mask_token": {
|
10 |
+
"content": "[MASK]",
|
11 |
+
"lstrip": true,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"pad_token": {
|
17 |
+
"content": "[PAD]",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"sep_token": {
|
24 |
+
"content": "[SEP]",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"unk_token": {
|
31 |
+
"content": "[UNK]",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
}
|
37 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,945 @@
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"50363": {
|
892 |
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|
893 |
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|
894 |
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|
895 |
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|
896 |
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|
897 |
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"special": false
|
898 |
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},
|
899 |
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"50364": {
|
900 |
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"content": "[unused79]",
|
901 |
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|
902 |
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"normalized": true,
|
903 |
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|
904 |
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|
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|
906 |
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|
907 |
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"50365": {
|
908 |
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|
909 |
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|
910 |
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"normalized": true,
|
911 |
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|
912 |
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"single_word": false,
|
913 |
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|
914 |
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},
|
915 |
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"50366": {
|
916 |
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|
917 |
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"lstrip": false,
|
918 |
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|
919 |
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|
920 |
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"single_word": false,
|
921 |
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"special": false
|
922 |
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},
|
923 |
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"50367": {
|
924 |
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|
925 |
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|
926 |
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"normalized": true,
|
927 |
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|
928 |
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"single_word": false,
|
929 |
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"special": false
|
930 |
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}
|
931 |
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},
|
932 |
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"clean_up_tokenization_spaces": true,
|
933 |
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"cls_token": "[CLS]",
|
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"extra_special_tokens": {},
|
935 |
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"mask_token": "[MASK]",
|
936 |
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"model_input_names": [
|
937 |
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"input_ids",
|
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"attention_mask"
|
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
|
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|
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"tokenizer_class": "PreTrainedTokenizerFast",
|
944 |
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"unk_token": "[UNK]"
|
945 |
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}
|
training_args.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:eafadfe3235fd35684af65eb703480a94c2de5fb83254ba82c96884ad7cc0223
|
3 |
+
size 5432
|