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xlm-roberta-de

airnicco8 / xlm-roberta-de

Run on huggingface.co
huggingface.co
Total runs: 117
24-hour runs: 0
7-day runs: 33
30-day runs: 39
Model's Last Updated: October 19 2022
sentence-similarity

Introduction of xlm-roberta-de

Model Details of xlm-roberta-de

airnicco8/xlm-roberta-de

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is trained on the Ted talks transcripts filtered only by German language, the training setting is described here . It can be used straight-forwardly for sentence similarity, but can also be fine-tuned for NLI and Text-Classification, examples coming soon.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed: 

pip install -U sentence-transformers

Then you can use the model like this: 

from sentence_transformers import SentenceTransformer
sentences = ["das ist eine glückliche Frau", "das ist ein glücklicher Mann", "das ist ein glücklicher Hund"]

model = SentenceTransformer('airnicco8/xlm-roberta-de')
embeddings = model.encode(sentences)
print(embeddings)
Usage (HuggingFace Transformers)

Without sentence-transformers , you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. 

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ["das ist eine glückliche Frau", "das ist ein glücklicher Mann", "das ist ein glücklicher Hund"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('airnicco8/xlm-roberta-de')
model = AutoModel.from_pretrained('airnicco8/xlm-roberta-de')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark : https://seb.sbert.net

Training

The model was trained with the parameters:

DataLoader :

torch.utils.data.dataloader.DataLoader of length 3071 with parameters: 

{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss :

sentence_transformers.losses.MSELoss.MSELoss

Parameters of the fit()-Method: 

{
    "epochs": 10,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "eps": 1e-06,
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 1000,
    "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

Runs of airnicco8 xlm-roberta-de on huggingface.co

117
Total runs
0
24-hour runs
-6
3-day runs
33
7-day runs
39
30-day runs

More Information About xlm-roberta-de huggingface.co Model

xlm-roberta-de huggingface.co

xlm-roberta-de huggingface.co is an AI model on huggingface.co that provides xlm-roberta-de's model effect (), which can be used instantly with this airnicco8 xlm-roberta-de model. huggingface.co supports a free trial of the xlm-roberta-de model, and also provides paid use of the xlm-roberta-de. Support call xlm-roberta-de model through api, including Node.js, Python, http.

xlm-roberta-de huggingface.co Url

https://huggingface.co/airnicco8/xlm-roberta-de

airnicco8 xlm-roberta-de online free

xlm-roberta-de huggingface.co is an online trial and call api platform, which integrates xlm-roberta-de's modeling effects, including api services, and provides a free online trial of xlm-roberta-de, you can try xlm-roberta-de online for free by clicking the link below.

airnicco8 xlm-roberta-de online free url in huggingface.co:

https://huggingface.co/airnicco8/xlm-roberta-de

xlm-roberta-de install

xlm-roberta-de is an open source model from GitHub that offers a free installation service, and any user can find xlm-roberta-de on GitHub to install. At the same time, huggingface.co provides the effect of xlm-roberta-de install, users can directly use xlm-roberta-de installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

xlm-roberta-de install url in huggingface.co:

https://huggingface.co/airnicco8/xlm-roberta-de

Url of xlm-roberta-de

xlm-roberta-de huggingface.co Url

Provider of xlm-roberta-de huggingface.co

airnicco8
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huggingface.co

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