add AIBOM
#11
by
sabato-nocera
- opened
lxyuan_distilbert-base-multilingual-cased-sentiments-student.json
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{
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"bomFormat": "CycloneDX",
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"specVersion": "1.6",
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"serialNumber": "urn:uuid:8c3f1fac-7950-40e8-8977-5da6d50a3894",
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"version": 1,
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"metadata": {
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"timestamp": "2025-06-05T09:42:14.347334+00:00",
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"component": {
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"type": "machine-learning-model",
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"bom-ref": "lxyuan/distilbert-base-multilingual-cased-sentiments-student-145289ee-cb06-531e-a014-4563bb11c7b9",
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"name": "lxyuan/distilbert-base-multilingual-cased-sentiments-student",
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"externalReferences": [
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{
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"url": "https://huggingface.co/lxyuan/distilbert-base-multilingual-cased-sentiments-student",
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"type": "documentation"
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}
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],
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"modelCard": {
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"modelParameters": {
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"task": "text-classification",
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"architectureFamily": "distilbert",
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"modelArchitecture": "DistilBertForSequenceClassification",
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"datasets": [
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{
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"ref": "tyqiangz/multilingual-sentiments-a7a2b9dc-ddfc-5d28-92f2-358980efc9d2"
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}
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]
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},
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"properties": [
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{
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"name": "library_name",
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"value": "transformers"
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}
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]
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},
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"authors": [
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{
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"name": "lxyuan"
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}
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],
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"licenses": [
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{
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| 43 |
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"license": {
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"id": "Apache-2.0",
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"url": "https://spdx.org/licenses/Apache-2.0.html"
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}
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| 47 |
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}
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],
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"tags": [
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"transformers",
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"pytorch",
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"onnx",
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"safetensors",
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"distilbert",
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"text-classification",
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"sentiment-analysis",
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| 57 |
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"zero-shot-distillation",
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| 58 |
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"distillation",
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| 59 |
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"zero-shot-classification",
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"debarta-v3",
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"en",
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"ar",
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"de",
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"es",
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"fr",
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"ja",
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"zh",
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| 68 |
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"id",
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"hi",
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"it",
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"ms",
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"pt",
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"dataset:tyqiangz/multilingual-sentiments",
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"doi:10.57967/hf/1422",
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"license:apache-2.0",
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"autotrain_compatible",
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"endpoints_compatible",
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| 78 |
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"region:us"
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| 79 |
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]
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| 80 |
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}
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},
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"components": [
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{
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| 84 |
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"type": "data",
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| 85 |
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"bom-ref": "tyqiangz/multilingual-sentiments-a7a2b9dc-ddfc-5d28-92f2-358980efc9d2",
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| 86 |
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"name": "tyqiangz/multilingual-sentiments",
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"data": [
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{
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"type": "dataset",
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"bom-ref": "tyqiangz/multilingual-sentiments-a7a2b9dc-ddfc-5d28-92f2-358980efc9d2",
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"name": "tyqiangz/multilingual-sentiments",
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"contents": {
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"url": "https://huggingface.co/datasets/tyqiangz/multilingual-sentiments",
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"properties": [
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{
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"name": "task_categories",
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"value": "text-classification"
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},
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{
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"name": "task_ids",
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"value": "sentiment-analysis, sentiment-classification"
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},
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{
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"name": "language",
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"value": "de, en, es, fr, ja, zh, id, ar, hi, it, ms, pt"
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},
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{
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"name": "size_categories",
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"value": "100K<n<1M, 1M<n<10M"
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},
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{
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"name": "license",
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"value": "apache-2.0"
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}
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]
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},
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"governance": {
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"owners": [
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{
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"organization": {
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"name": "tyqiangz",
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"url": "https://huggingface.co/tyqiangz"
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}
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}
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]
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},
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"description": "\n\t\n\t\t\n\t\tMultilingual Sentiments Dataset\n\t\n\nA collection of multilingual sentiments datasets grouped into 3 classes -- positive, neutral, negative.\nMost multilingual sentiment datasets are either 2-class positive or negative, 5-class ratings of products reviews (e.g. Amazon multilingual dataset) or multiple classes of emotions. However, to an average person, sometimes positive, negative and neutral classes suffice and are more straightforward to perceive and annotate. Also, a positive/negative\u2026 See the full description on the dataset page: https://huggingface.co/datasets/tyqiangz/multilingual-sentiments."
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}
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]
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}
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]
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| 132 |
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}
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