Instructions to use MM2157/fold_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MM2157/fold_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MM2157/fold_4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MM2157/fold_4") model = AutoModelForSequenceClassification.from_pretrained("MM2157/fold_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Sussex_AI_marbert_arabic_sentiment_5F
Browse files- README.md +62 -0
- config.json +47 -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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base_model: UBC-NLP/MARBERT
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tags:
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- generated_from_trainer
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model-index:
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- name: fold_4
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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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# fold_4
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This model is a fine-tuned version of [UBC-NLP/MARBERT](https://huggingface.co/UBC-NLP/MARBERT) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4025
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- Macro F1: 0.8920
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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: 1e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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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: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Macro F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.9836 | 1.0 | 87 | 0.5345 | 0.7540 |
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| 0.3982 | 2.0 | 174 | 0.3769 | 0.8723 |
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| 0.2025 | 3.0 | 261 | 0.3329 | 0.8814 |
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| 0.1355 | 4.0 | 348 | 0.3793 | 0.8971 |
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| 0.0506 | 5.0 | 435 | 0.4025 | 0.8920 |
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### Framework versions
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- Transformers 5.0.0
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- Pytorch 2.9.0+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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"add_cross_attention": false,
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": null,
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"classifier_dropout": null,
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"directionality": "bidi",
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"dtype": "float32",
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"eos_token_id": null,
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"gradient_checkpointing": false,
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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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"id2label": {
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"0": "negative",
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"1": "neutral",
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"2": "positive"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"is_decoder": false,
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"label2id": {
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"negative": 0,
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"neutral": 1,
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"positive": 2
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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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_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"type_vocab_size": 2,
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"use_cache": false,
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"vocab_size": 100000
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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:acf2ca798af5c1a447dc304e41dc07891b57e4c5d2c16347fd7caee872f367e8
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size 651398124
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:91436a227e705dde77bd341d9fdf559d3c40210cadc424d646101999f4b53170
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size 5137
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