Upload 13 files
Browse files- README.md +113 -3
- added_tokens.json +3 -0
- all_results.json +16 -0
- config.json +35 -0
- eval_results.json +10 -0
- model.safetensors +3 -0
- special_tokens_map.json +15 -0
- spm.model +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +58 -0
- train_results.json +9 -0
- trainer_state.json +2485 -0
- training_args.bin +3 -0
README.md
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---
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language: en
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license: mit
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library_name: transformers
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tags:
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- generated_from_trainer
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- text-classification
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- fill-mask
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- embeddings
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metrics:
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- accuracy
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model-index:
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- name: deberta-v3-base-zyda-2
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results:
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- task:
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type: text-classification
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name: Text Classification
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dataset:
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name: Zyphra/Zyda-2 (subset)
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type: Zyphra/Zyda-2
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metrics:
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- type: accuracy
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value: 0.6191
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name: Accuracy
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base_model: microsoft/deberta-v3-base
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---
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# DeBERTa-v3-base-Zyda-2
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## Model Description
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on a subset of the [Zyphra/Zyda-2](https://huggingface.co/datasets/Zyphra/Zyda-2) dataset. It was trained using the Masked Language Modeling (MLM) objective to enhance its understanding of the English language.
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## Performance
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The model achieves the following results on the evaluation set:
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- Loss: 2.1833
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- Accuracy: 0.6191
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## Intended Uses & Limitations
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This model is designed to be used and finetuned for the following tasks:
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- Text embedding
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- Text classification
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- Fill-in-the-blank tasks
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**Limitations:**
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- English language only
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- May be inaccurate for specialized jargon, dialects, slang, code, and LaTeX
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## Training Data
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The model was trained on the first 300 000 rows of the [Zyphra/Zyda-2](https://huggingface.co/datasets/Zyphra/Zyda-2) dataset.
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5% of that data was used for validation.
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## Training Procedure
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### Hyperparameters
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The following hyperparameters were used during training:
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- Learning rate: 5e-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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- Optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- Learning rate scheduler: Linear
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- Number of epochs: 1.0
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### Framework versions
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- Transformers: 4.46.3
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- Pytorch: 2.5.1+cu124
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- Datasets: 3.1.0
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- Tokenizers: 0.20.3
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## Usage Examples
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### Masked Language Modeling
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```python
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from transformers import pipeline
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unmasker = pipeline('fill-mask', model='agentlans/deberta-v3-base-zyda-2')
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result = unmasker("[MASK] is the capital of France.")
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print(result)
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```
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### Text Embedding
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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model_name = "agentlans/deberta-v3-base-zyda-2"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModel.from_pretrained(model_name)
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text = "Example sentence for embedding."
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inputs = tokenizer(text, return_tensors='pt')
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with torch.no_grad():
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outputs = model(**inputs)
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embeddings = outputs.last_hidden_state.mean(dim=1)
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print(embeddings)
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```
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## Ethical Considerations and Bias
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As this model is trained on a subset of the Zyda-2 dataset, it may inherit biases present in that data. Users should be aware of potential biases and evaluate the model's output critically, especially for sensitive applications.
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## Additional Information
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For more details about the base model, please refer to [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base).
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added_tokens.json
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{
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"[MASK]": 128000
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}
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all_results.json
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{
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"epoch": 1.0,
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"eval_accuracy": 0.6191083101642154,
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"eval_loss": 2.1832997798919678,
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"eval_runtime": 619.8106,
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"eval_samples": 36612,
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"eval_samples_per_second": 59.07,
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"eval_steps_per_second": 7.385,
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"perplexity": 8.875545336961899,
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"total_flos": 1.8427441878551347e+17,
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"train_loss": 1.5726176189089465,
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"train_runtime": 27622.4465,
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"train_samples": 699309,
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"train_samples_per_second": 25.317,
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"train_steps_per_second": 6.329
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}
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config.json
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{
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"_name_or_path": "microsoft/deberta-v3-base",
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"architectures": [
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"DebertaV2ForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-07,
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"max_position_embeddings": 512,
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"max_relative_positions": -1,
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"model_type": "deberta-v2",
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"norm_rel_ebd": "layer_norm",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"pooler_dropout": 0,
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"pooler_hidden_act": "gelu",
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"pooler_hidden_size": 768,
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"pos_att_type": [
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"p2c",
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"c2p"
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],
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"position_biased_input": false,
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"position_buckets": 256,
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"relative_attention": true,
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"share_att_key": true,
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"torch_dtype": "float32",
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"transformers_version": "4.46.3",
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"type_vocab_size": 0,
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"vocab_size": 128100
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}
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eval_results.json
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{
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"epoch": 1.0,
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"eval_accuracy": 0.6191083101642154,
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"eval_loss": 2.1832997798919678,
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"eval_runtime": 619.8106,
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"eval_samples": 36612,
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"eval_samples_per_second": 59.07,
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"eval_steps_per_second": 7.385,
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"perplexity": 8.875545336961899
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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 sha256:1c6027fb50e1e57a250247d1bd078ba866085b473710d84a431d2a6996756fd6
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size 738231856
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special_tokens_map.json
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{
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"bos_token": "[CLS]",
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"cls_token": "[CLS]",
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"eos_token": "[SEP]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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}
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}
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spm.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
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size 2464616
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tokenizer.json
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"128000": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"bos_token": "[CLS]",
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"clean_up_tokenization_spaces": false,
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"cls_token": "[CLS]",
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"do_lower_case": false,
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"eos_token": "[SEP]",
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"sp_model_kwargs": {},
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"split_by_punct": false,
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"tokenizer_class": "DebertaV2Tokenizer",
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"unk_token": "[UNK]",
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"vocab_type": "spm"
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}
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train_results.json
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|
| 1 |
+
{
|
| 2 |
+
"epoch": 1.0,
|
| 3 |
+
"total_flos": 1.8427441878551347e+17,
|
| 4 |
+
"train_loss": 1.5726176189089465,
|
| 5 |
+
"train_runtime": 27622.4465,
|
| 6 |
+
"train_samples": 699309,
|
| 7 |
+
"train_samples_per_second": 25.317,
|
| 8 |
+
"train_steps_per_second": 6.329
|
| 9 |
+
}
|
trainer_state.json
ADDED
|
@@ -0,0 +1,2485 @@
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