Instructions to use Vedansh-Gupta/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vedansh-Gupta/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Vedansh-Gupta/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Vedansh-Gupta/results") model = AutoModelForSequenceClassification.from_pretrained("Vedansh-Gupta/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Vedansh-Gupta/depression-distilbert
Browse files- README.md +63 -0
- config.json +28 -0
- model.safetensors +3 -0
- training_args.bin +3 -0
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: distilbert-base-uncased
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: results
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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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# results
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0611
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- Accuracy: 0.9893
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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: 16
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.1046 | 1.0 | 721 | 0.0664 | 0.9820 |
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| 0.0402 | 2.0 | 1442 | 0.0504 | 0.9896 |
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| 0.0046 | 3.0 | 2163 | 0.0611 | 0.9893 |
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### Framework versions
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- Transformers 5.15.0
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- Pytorch 2.11.0+cu128
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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config.json
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{
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"bos_token_id": null,
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"dim": 768,
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"dropout": 0.1,
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"dtype": "float32",
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"eos_token_id": null,
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"hidden_dim": 3072,
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"initializer_range": 0.02,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"tie_word_embeddings": true,
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"transformers_version": "5.15.0",
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"use_cache": false,
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"vocab_size": 30522
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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 sha256:2f89a6410f913c9a3ad45e1b640ab3d5b30f7b83d2359f16ab0817d7609a82de
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size 267832560
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:29d811cdeb5b88771c4a6e09b5305ea0c206281e632021423b1b05c1843b914d
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size 5201
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