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---
license: mit
base_model:
- sentence-transformers/paraphrase-multilingual-mpnet-base-v2
---
# protestforms_mpnet-base-v2
This is a fine-tuned [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
It was trained on a manually annotated dataset of German newspaper articles containing information on protest forms.
## Usage (Sentence-Transformers)
```python
from sentence_transformers import SentenceTransformer
sentences = ["At 8pm protesters gathered on the main square and shouted 'end fossil fuels'", "The German government demonstrated composure in its reaction to social media posts"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
# Sentences we want sentence embeddings for
sentences = ["At 8pm protesters gathered on the main square and shouted 'end fossil fuels'", "The German government demonstrated composure in its reaction to social media posts"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('shaunss/protestforms_mpnet-base-v2')
model = AutoModel.from_pretrained('shaunss/protestforms_mpnet-base-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
```
<!--- Describe how your model was evaluated -->
<!--- t.b.d. -->
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 681 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.BatchSemiHardTripletLoss.BatchSemiHardTripletLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 2177.5,
"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2177.5,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |