mdeputy/output
Browse files- README.md +65 -66
- config.json +31 -31
- model.safetensors +1 -1
- training_args.bin +2 -2
README.md
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---
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tags:
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- generated_from_trainer
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metrics:
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- f1
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- precision
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- recall
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- accuracy
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model-index:
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- name: output
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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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- Tokenizers 0.19.1
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---
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tags:
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- generated_from_trainer
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metrics:
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- f1
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- precision
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- recall
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- accuracy
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model-index:
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- name: output
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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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# output
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.2780
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- F1: 0.0
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- Precision: 0.0
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- Recall: 0.0
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- Accuracy: 0.0
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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: 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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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 100
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---:|:---------:|:------:|:--------:|
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| No log | 1.0 | 12 | 1.2780 | 0.0 | 0.0 | 0.0 | 0.0 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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config.json
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{
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"architectures": [
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"AttAlexNetForClassification"
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],
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"attention_type": "sdpa",
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"classifier_dim": 4096,
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"classifier_dropout": 0.1,
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"dual_obj": false,
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"hidden_act": "silu",
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"hidden_size": 64,
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"img_size": 1024,
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"in_channels": 3,
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"intermediate_size": 1024,
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"is_causal": false,
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"max_position_embeddings": 4096,
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"model_type": "att_alexnet",
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"moe": false,
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"n_filts": 4,
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"num_attention_heads": 8,
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"num_classes": 3,
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"num_experts": 8,
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"num_hidden_layers": 6,
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"num_layers": 2,
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"output_router_logits": true,
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"patch_size": 16,
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"problem_type": "single_label_classification",
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"router_aux_loss_coef": 0.01,
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"topk": 2,
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"torch_dtype": "float32",
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"transformers_version": "4.
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}
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{
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"architectures": [
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"AttAlexNetForClassification"
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],
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"attention_type": "sdpa",
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"classifier_dim": 4096,
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"classifier_dropout": 0.1,
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"dual_obj": false,
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"hidden_act": "silu",
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"hidden_size": 64,
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"img_size": 1024,
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"in_channels": 3,
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"intermediate_size": 1024,
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"is_causal": false,
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"max_position_embeddings": 4096,
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"model_type": "att_alexnet",
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"moe": false,
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"n_filts": 4,
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"num_attention_heads": 8,
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"num_classes": 3,
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"num_experts": 8,
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"num_hidden_layers": 6,
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"num_layers": 2,
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"output_router_logits": true,
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"patch_size": 16,
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"problem_type": "single_label_classification",
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"router_aux_loss_coef": 0.01,
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"topk": 2,
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"torch_dtype": "float32",
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"transformers_version": "4.41.2"
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}
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model.safetensors
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training_args.bin
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size
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