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CBertbase-mlm-APPS10k
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- config.json +4 -2
README.md
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tags:
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---
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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base_model: microsoft/codebert-base-mlm
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tags:
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- generated_from_trainer
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model-index:
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- name: CBertbase-mlm-APPS10k
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results: []
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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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# CBertbase-mlm-APPS10k
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This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0911
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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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: 5e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 100
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- training_steps: 10000
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:-----:|:---------------:|
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| 2.1352 | 0.05 | 500 | 1.8774 |
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| 1.1754 | 0.1 | 1000 | 1.4835 |
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| 1.4127 | 0.15 | 1500 | 1.4475 |
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| 1.0497 | 0.2 | 2000 | 1.3342 |
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| 1.0403 | 0.25 | 2500 | 1.2589 |
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| 1.0754 | 0.3 | 3000 | 1.2174 |
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| 0.8836 | 0.35 | 3500 | 1.2265 |
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| 0.95 | 0.4 | 4000 | 1.1931 |
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| 1.0324 | 0.45 | 4500 | 1.1729 |
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| 1.0296 | 0.5 | 5000 | 1.1462 |
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| 0.886 | 0.55 | 5500 | 1.1364 |
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| 0.9352 | 0.6 | 6000 | 1.1201 |
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| 1.0547 | 0.65 | 6500 | 1.1481 |
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| 0.8277 | 0.7 | 7000 | 1.1128 |
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| 0.8685 | 0.75 | 7500 | 1.1153 |
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| 0.9277 | 0.8 | 8000 | 1.1194 |
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| 0.8111 | 0.85 | 8500 | 1.0975 |
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| 0.9345 | 0.9 | 9000 | 1.0913 |
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| 0.9166 | 0.95 | 9500 | 1.0904 |
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| 0.952 | 1.0 | 10000 | 1.0911 |
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### Framework versions
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- Transformers 4.38.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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config.json
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{
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"_name_or_path": "microsoft/codebert-base-mlm",
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"architectures": [
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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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-05,
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"output_past": true,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"transformers_version": "4.38.2",
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"type_vocab_size": 1,
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"use_cache":
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"vocab_size": 50265
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}
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{
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"_name_or_path": "microsoft/codebert-base-mlm",
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"architectures": [
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"RobertaForCausalLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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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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"is_decoder": true,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"output_past": true,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.38.2",
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"type_vocab_size": 1,
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"use_cache": false,
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"vocab_size": 50265
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}
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