End of training
Browse files- README.md +38 -183
- config.json +82 -0
- model.safetensors +3 -0
- training_args.bin +3 -0
README.md
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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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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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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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[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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#### 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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### Compute Infrastructure
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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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**APA:**
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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license: mit
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base_model: facebook/w2v-bert-2.0
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tags:
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- generated_from_trainer
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datasets:
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- common_voice_16_0
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model-index:
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- name: w2v-bert-2.0-bangala-gpu-CV16.0_v3
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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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# w2v-bert-2.0-bangala-gpu-CV16.0_v3
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This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_16_0 dataset.
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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: 4.42184e-05
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- train_batch_size: 16
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 10
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.39.3
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- Pytorch 2.1.2+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.0
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config.json
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{
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"_name_or_path": "facebook/w2v-bert-2.0",
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"activation_dropout": 0.0,
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"adapter_act": "relu",
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"adapter_kernel_size": 3,
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"adapter_stride": 2,
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"add_adapter": true,
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"apply_spec_augment": false,
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"architectures": [
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"Wav2Vec2BertForCTC"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"classifier_proj_size": 768,
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"codevector_dim": 768,
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"conformer_conv_dropout": 0.1,
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"contrastive_logits_temperature": 0.1,
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"conv_depthwise_kernel_size": 31,
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"ctc_loss_reduction": "mean",
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"ctc_zero_infinity": false,
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"diversity_loss_weight": 0.1,
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"eos_token_id": 2,
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"feat_proj_dropout": 0.0,
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"feat_quantizer_dropout": 0.0,
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"feature_projection_input_dim": 160,
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"final_dropout": 0.1,
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"hidden_act": "swish",
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"hidden_dropout": 0.0,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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"layerdrop": 0.1,
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"left_max_position_embeddings": 64,
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"mask_feature_length": 10,
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"mask_feature_min_masks": 0,
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"mask_feature_prob": 0.0,
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"mask_time_length": 10,
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"mask_time_min_masks": 2,
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"mask_time_prob": 0.05,
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"max_source_positions": 5000,
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"model_type": "wav2vec2-bert",
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"num_adapter_layers": 1,
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"num_attention_heads": 16,
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"num_codevector_groups": 2,
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"num_codevectors_per_group": 320,
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"num_hidden_layers": 24,
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"num_negatives": 100,
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"output_hidden_size": 1024,
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"pad_token_id": 72,
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"position_embeddings_type": "relative_key",
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"proj_codevector_dim": 768,
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"right_max_position_embeddings": 8,
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"rotary_embedding_base": 10000,
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"tdnn_dilation": [
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1,
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2,
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3,
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1,
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1
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],
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"tdnn_dim": [
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512,
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512,
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512,
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512,
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1500
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],
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"tdnn_kernel": [
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3,
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3,
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1,
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1
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],
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"torch_dtype": "float32",
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"transformers_version": "4.39.3",
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"use_intermediate_ffn_before_adapter": false,
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"use_weighted_layer_sum": false,
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"vocab_size": 75,
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"xvector_output_dim": 512
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}
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version https://git-lfs.github.com/spec/v1
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oid sha256:b55262fe4a445ab452aeff4858106d1cd7c1b9c996e0fceb246d9133e13ae01b
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size 2423126260
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:c3f8ff59098954a3de2181e0a90ab793209c8e90214cdbd382aeecdd141e7770
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size 4920
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