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AlignScoreCS

Text Classification
Transformers
PyTorch
English
Czech
xlm-roberta
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Merge branch 'main' of https://huggingface.co/krotima1/AlignScoreCS

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  1. .gitattributes +1 -0
  2. README.md +75 -1
  3. special_tokens_map.json +15 -0
  4. tokenizer.json +3 -0
  5. tokenizer_config.json +54 -0
.gitattributes CHANGED
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README.md CHANGED
@@ -1 +1,75 @@
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- hello
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ language:
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+ - en
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+ - cs
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+ license: cc-by-4.0
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+ metrics:
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+ - bleurt
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+ - bleu
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+ - bertscore
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+ pipeline_tag: text-classification
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+ ---
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+ # AlignScoreCS
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+ MultiTask multilingual model for assessing facticity in various NLU tasks in Czech and English language. We followed the initial paper AlignScore https://arxiv.org/abs/2305.16739.
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+ We trained a model using a shared architecture of checkpoint xlm-roberta-large https://huggingface.co/FacebookAI/xlm-roberta-large with three linear layers for regression,
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+ binary classification and ternary classification.
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+
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+
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+ # Usage
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+ ```python
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+ # Assuming you copied the attached Files_and_versions/AlignScore.py file for ease of use in transformers.
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+ from AlignScoreCS import AlignScoreCS
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+ alignScoreCS = AlignScoreCS.from_pretrained("krotima1/AlignScoreCS")
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+ # put the model to cuda to accelerate
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+ print(alignScoreCS.score(context="This is context", claim="This is claim"))
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+
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+ ```
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+
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+ # Results
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+
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+
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+
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+ # Training datasets
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+ The following table shows datasets that has been utilized for training the model. We translated these english datasets to Czech using seamLessM4t.
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+
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+ | NLP Task | Dataset | Training Task | Context (n words) | Claim (n words) | Sample Count |
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+ |-----------------------|-------------------|---------------|-------------------|-----------------|--------------|
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+ | NLI | SNLI | 3-way | 10 | 13 | Cs: 500k |
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+ | | | | | | En: 550k |
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+ | | MultiNLI | 3-way | 16 | 20 | Cs: 393k |
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+ | | | | | | En: 393k |
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+ | | Adversarial NLI | 3-way | 48 | 54 | Cs: 163k |
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+ | | | | | | En: 163k |
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+ | | DocNLI | 2-way | 97 | 285 | Cs: 200k |
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+ | | | | | | En: 942k |
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+ | Fact Verification | NLI-style FEVER | 3-way | 48 | 50 | Cs: 208k |
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+ | | | | | | En: 208k |
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+ | | Vitamin C | 3-way | 23 | 25 | Cs: 371k |
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+ | | | | | | En: 371k |
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+ | Paraphrase | QQP | 2-way | 9 | 11 | Cs: 162k |
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+ | | | | | | En: 364k |
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+ | | PAWS | 2-way | - | 18 | Cs: - |
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+ | | | | | | En: 707k |
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+ | | PAWS labeled | 2-way | 18 | - | Cs: 49k |
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+ | | | | | | En: - |
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+ | | PAWS unlabeled | 2-way | 18 | - | Cs: 487k |
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+ | | | | | | En: - |
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+ | STS | SICK | reg | - | 10 | Cs: - |
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+ | | | | | | En: 4k |
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+ | | STS Benchmark | reg | - | 10 | Cs: - |
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+ | | | | | | En: 6k |
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+ | | Free-N1 | reg | 18 | - | Cs: 20k |
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+ | | | | | | En: - |
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+ | QA | SQuAD v2 | 2-way | 105 | 119 | Cs: 130k |
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+ | | | | | | En: 130k |
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+ | | RACE | 2-way | 266 | 273 | Cs: 200k |
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+ | | | | | | En: 351k |
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+ | Information Retrieval| MS MARCO | 2-way | 49 | 56 | Cs: 200k |
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+ | | | | | | En: 5M |
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+ | Summarization | WikiHow | 2-way | 434 | 508 | Cs: 157k |
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+ | | | | | | En: 157k |
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+ | | SumAug | 2-way | - | - | Cs: - |
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+ | | | | | | En: - |
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+
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+
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+
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