Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Zainab984/BP-test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zainab984/BP-test1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Zainab984/BP-test1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Zainab984/BP-test1") model = AutoModelForSequenceClassification.from_pretrained("Zainab984/BP-test1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| No log | 0.01 | 50 | 0.
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| No log | 0.03 | 150 | 0.
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| No log | 0.07 | 350 | 0.
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| 0.6127 | 0.16 | 800 | 0.1944 | 0.95 |
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| 0.6127 | 0.17 | 850 | 0.1942 | 0.95 |
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| 0.6127 | 0.18 | 900 | 0.1938 | 0.95 |
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| 0.6127 | 0.19 | 950 | 0.1950 | 0.95 |
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| 0.2388 | 0.2 | 1000 | 0.1943 | 0.95 |
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| 0.2388 | 0.2 | 1050 | 0.1939 | 0.95 |
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| 0.2388 | 0.21 | 1100 | 0.1939 | 0.95 |
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| 0.2388 | 0.22 | 1150 | 0.1928 | 0.95 |
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| 0.2388 | 0.23 | 1200 | 0.1937 | 0.95 |
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| 0.2388 | 0.24 | 1250 | 0.1958 | 0.95 |
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### Framework versions
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2385
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- Accuracy: 0.93
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| No log | 0.01 | 50 | 0.6925 | 0.45 |
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| No log | 0.02 | 100 | 0.6758 | 0.62 |
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| No log | 0.03 | 150 | 0.6384 | 0.66 |
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| No log | 0.04 | 200 | 0.6241 | 0.66 |
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| No log | 0.05 | 250 | 0.6081 | 0.66 |
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| No log | 0.06 | 300 | 0.5643 | 0.87 |
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| No log | 0.07 | 350 | 0.5200 | 0.87 |
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| No log | 0.08 | 400 | 0.4989 | 0.81 |
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| No log | 0.09 | 450 | 0.4370 | 0.87 |
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| 0.6058 | 0.1 | 500 | 0.3298 | 0.94 |
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| 0.6058 | 0.11 | 550 | 0.2876 | 0.94 |
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| 0.6058 | 0.12 | 600 | 0.2701 | 0.93 |
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| 0.6058 | 0.13 | 650 | 0.2533 | 0.93 |
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| 0.6058 | 0.14 | 700 | 0.2452 | 0.93 |
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| 0.6058 | 0.15 | 750 | 0.2385 | 0.93 |
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### Framework versions
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config.json
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{
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"_name_or_path": "prajjwal1/bert-tiny",
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 128,
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"id2label": {
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"0": "Taken",
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"1": "Not Taken"
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},
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"initializer_range": 0.02,
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"intermediate_size": 512,
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"label2id": {
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"Not Taken": 1,
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"Taken": 0
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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_hidden_layers": 2,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.35.2",
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"type_vocab_size": 2,
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"use_cache": true,
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{
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"_name_or_path": "prajjwal1/bert-tiny",
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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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"classifier_dropout": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 128,
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"initializer_range": 0.02,
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"intermediate_size": 512,
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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_hidden_layers": 2,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.35.2",
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"type_vocab_size": 2,
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"use_cache": true,
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