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Browse files- README.md +83 -0
- config.json +26 -0
- lightning_logs/version_0/events.out.tfevents.1678982684.ki-jupyternotebook-8bdd +3 -0
- lightning_logs/version_0/hparams.yaml +1 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +16 -0
- vocab.txt +0 -0
README.md
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---
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license: cc-by-nc-4.0
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pipeline_tag: question-answering
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tags:
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- question-answering
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- transformers
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- generated_from_trainer
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datasets:
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- squad_v2
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- LLukas22/nq-simplified
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- LLukas22/NLQuAD
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- deepset/germanquad
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---
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# all-MiniLM-L12-v2-qa-all
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This model is an extractive qa model.
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It's a fine-tuned version of [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) on the following datasets: [squad_v2](https://huggingface.co/datasets/squad_v2), [LLukas22/nq-simplified](https://huggingface.co/datasets/LLukas22/nq-simplified), [LLukas22/NLQuAD](https://huggingface.co/datasets/LLukas22/NLQuAD), [deepset/germanquad](https://huggingface.co/datasets/deepset/germanquad).
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## Usage
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You can use the model like this:
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```python
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from transformers import pipeline
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#Make predictions
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model_name = "LLukas22/all-MiniLM-L12-v2-qa-all"
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
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QA_input = {
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"question": "What's my name?",
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"context": "My name is Clara and I live in Berkeley."
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}
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result = nlp(QA_input)
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print(result)
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```
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Alternatively you can load the model and tokenizer on their own:
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```python
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer
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#Make predictions
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model_name = "LLukas22/all-MiniLM-L12-v2-qa-all"
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model = AutoModelForQuestionAnswering.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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```
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## Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2E-05
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- per device batch size: 60
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- effective batch size: 180
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- seed: 42
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- optimizer: AdamW with betas (0.9,0.999) and eps 1E-08
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- weight decay: 1E-02
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- D-Adaptation: False
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- Warmup: True
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- number of epochs: 15
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- mixed_precision_training: bf16
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## Training results
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| Epoch | Train Loss | Validation Loss |
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| ----- | ---------- | --------------- |
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| 0 | 3.58 | 2.91 |
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## Evaluation results
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| Epoch | f1 | exact_match |
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| ----- | ----- | ----- |
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| 0 | 0.309 | 0.255 |
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## Framework versions
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- Transformers: 4.25.1
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- PyTorch: 2.0.0.dev20230210+cu118
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- PyTorch Lightning: 1.8.6
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- Datasets: 2.7.1
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- Tokenizers: 0.13.1
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- Sentence Transformers: 2.2.2
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## Additional Information
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This model was trained as part of my Master's Thesis **'Evaluation of transformer based language models for use in service information systems'**. The source code is available on [Github](https://github.com/LLukas22/Master).
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config.json
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{
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"_name_or_path": "sentence-transformers/all-MiniLM-L12-v2",
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"architectures": [
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"BertForQuestionAnswering"
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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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"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": 384,
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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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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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.25.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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lightning_logs/version_0/events.out.tfevents.1678982684.ki-jupyternotebook-8bdd
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version https://git-lfs.github.com/spec/v1
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oid sha256:5c6f09fdca5357682372064d6a90c1021e64037f1f5b68b1b41e8338faa3e8a3
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size 5074
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lightning_logs/version_0/hparams.yaml
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{}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 132923885
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"name_or_path": "sentence-transformers/all-MiniLM-L12-v2",
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": "/home/jovyan/_huggingface-shared/hub/models--sentence-transformers--all-MiniLM-L12-v2/snapshots/9e16800aed25dbd1a96dfa6949c68c4d81d5dded/special_tokens_map.json",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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