Datasets:

ftopal

/

huggingface-models-processed

like 0

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2175 - Accuracy: 0.9215 - F1: 0.9216 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7814 | 1.0 | 250 | 0.3105 | 0.907 | 0.9046 | | 0.2401 | 2.0 | 500 | 0.2175 | 0.9215 | 0.9216 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9215, "name": "Accuracy"}, {"type": "f1", "value": 0.9215538311282218, "name": "F1"}]}]}]}

Crives/distilbert-base-uncased-finetuned-emotion

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

distilbert-base-uncased-finetuned-emotion ========================================= This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set: * Loss: 0.2175 * Accuracy: 0.9215 * F1: 0.9216 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.10.0+cu111 * Datasets 1.16.1 * Tokenizers 0.10.3

text-generation

transformers

#rick DialoGPT Model

{"tags": ["conversational"]}

Cryptikdw/DialoGPT-small-rick

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#rick DialoGPT Model

text-generation

transformers

# Paladin Danse DialoGPT Model

{"tags": ["conversational"]}

Cthyllax/DialoGPT-medium-PaladinDanse

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Paladin Danse DialoGPT Model

token-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # IceBERT-finetuned-ner This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0807 - Precision: 0.8927 - Recall: 0.8632 - F1: 0.8777 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0544 | 1.0 | 2904 | 0.0774 | 0.8859 | 0.8490 | 0.8670 | 0.9833 | | 0.0284 | 2.0 | 5808 | 0.0781 | 0.8709 | 0.8590 | 0.8649 | 0.9840 | | 0.0166 | 3.0 | 8712 | 0.0807 | 0.8927 | 0.8632 | 0.8777 | 0.9850 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

{"license": "gpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "IceBERT-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "type": "mim_gold_ner", "args": "mim-gold-ner"}, "metrics": [{"type": "precision", "value": 0.8927335640138409, "name": "Precision"}, {"type": "recall", "value": 0.8631855657784682, "name": "Recall"}, {"type": "f1", "value": 0.8777109531620194, "name": "F1"}, {"type": "accuracy", "value": 0.9849836396073506, "name": "Accuracy"}]}]}]}

Culmenus/IceBERT-finetuned-ner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

IceBERT-finetuned-ner ===================== This model is a fine-tuned version of vesteinn/IceBERT on the mim\_gold\_ner dataset. It achieves the following results on the evaluation set: * Loss: 0.0807 * Precision: 0.8927 * Recall: 0.8632 * F1: 0.8777 * Accuracy: 0.9850 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.11.2 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3

token-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # XLMR-ENIS-finetuned-ner This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0891 - Precision: 0.8804 - Recall: 0.8517 - F1: 0.8658 - Accuracy: 0.9837 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0573 | 1.0 | 2904 | 0.1024 | 0.8608 | 0.8003 | 0.8295 | 0.9799 | | 0.0307 | 2.0 | 5808 | 0.0899 | 0.8707 | 0.8380 | 0.8540 | 0.9825 | | 0.0198 | 3.0 | 8712 | 0.0891 | 0.8804 | 0.8517 | 0.8658 | 0.9837 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

{"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "XLMR-ENIS-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "type": "mim_gold_ner", "args": "mim-gold-ner"}, "metrics": [{"type": "precision", "value": 0.8803619696791632, "name": "Precision"}, {"type": "recall", "value": 0.8517339397384878, "name": "Recall"}, {"type": "f1", "value": 0.8658113730929264, "name": "F1"}, {"type": "accuracy", "value": 0.9837103244207861, "name": "Accuracy"}]}]}]}

Culmenus/XLMR-ENIS-finetuned-ner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

XLMR-ENIS-finetuned-ner ======================= This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim\_gold\_ner dataset. It achieves the following results on the evaluation set: * Loss: 0.0891 * Precision: 0.8804 * Recall: 0.8517 * F1: 0.8658 * Accuracy: 0.9837 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.11.2 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3

automatic-speech-recognition

transformers

# Wav2Vec2-Large-XLSR-53-Vietnamese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice), [Infore_25h dataset](https://files.huylenguyen.com/25hours.zip) (Password: BroughtToYouByInfoRe) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "vi", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietnamese") model = Wav2Vec2ForCTC.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietnamese") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Vietnamese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "vi", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietnamese") model = Wav2Vec2ForCTC.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietnamese") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 58.63 % ## Training The Common Voice `train`, `validation`, and `Infore_25h` datasets were used for training The script used for training can be found [here](https://drive.google.com/file/d/1AW9R8IlsapiSGh9n3aECf23t-zhk3wUh/view?usp=sharing) =======================To here===============================> Your model in then available under *huggingface.co/CuongLD/wav2vec2-large-xlsr-vietnamese* for everybody to use 🎉. ## How to evaluate my trained checkpoint Having uploaded your model, you should now evaluate your model in a final step. This should be as simple as copying the evaluation code of your model card into a python script and running it. Make sure to note the final result on the model card **both** under the YAML tags at the very top **and** below your evaluation code under "Test Results". ## Rules of training and evaluation In this section, we will quickly go over what data is allowed to be used as training data, what kind of data preprocessing is allowed be used, and how the model should be evaluated. To make it very simple regarding the first point: **All data except the official common voice `test` data set can be used as training data**. For models trained in a language that is not included in Common Voice, the author of the model is responsible to leave a reasonable amount of data for evaluation. Second, the rules regarding the preprocessing are not that as straight-forward. It is allowed (and recommended) to normalize the data to only have lower-case characters. It is also allowed (and recommended) to remove typographical symbols and punctuation marks. A list of such symbols can *e.g.* be fonud [here](https://en.wikipedia.org/wiki/List_of_typographical_symbols_and_punctuation_marks) - however here we already must be careful. We should **not** remove a symbol that would change the meaning of the words, *e.g.* in English, we should not remove the single quotation mark `'` since it would change the meaning of the word `"it's"` to `"its"` which would then be incorrect. So the golden rule here is to not remove any characters that could change the meaning of a word into another word. This is not always obvious and should be given some consideration. As another example, it is fine to remove the "Hypen-minus" sign "`-`" since it doesn't change the meaninng of a word to another one. *E.g.* "`fine-tuning`" would be changed to "`finetuning`" which has still the same meaning. Since those choices are not always obvious when in doubt feel free to ask on Slack or even better post on the forum, as was done, *e.g.* [here](https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586). ## Tips and tricks This section summarizes a couple of tips and tricks across various topics. It will continously be updated during the week. ### How to combine multiple datasets into one Check out [this](https://discuss.huggingface.co/t/how-to-combine-local-data-files-with-an-official-dataset/4685) post. ### How to effectively preprocess the data ### How to do efficiently load datasets with limited ram and hard drive space Check out [this](https://discuss.huggingface.co/t/german-asr-fine-tuning-wav2vec2/4558/8?u=patrickvonplaten) post. ### How to do hyperparameter tuning ### How to preprocess and evaluate character based languages ## Further reading material It is recommended that take some time to read up on how Wav2vec2 works in theory. Getting a better understanding of the theory and the inner mechanisms of the model often helps when fine-tuning the model. **However**, if you don't like reading blog posts/papers, don't worry - it is by no means necessary to go through the theory to fine-tune Wav2Vec2 on your language of choice. If you are interested in learning more about the model though, here are a couple of resources that are important to better understand Wav2Vec2: - [Facebook's Wav2Vec2 blog post](https://ai.facebook.com/blog/wav2vec-state-of-the-art-speech-recognition-through-self-supervision/) - [Official Wav2Vec2 paper](https://arxiv.org/abs/2006.11477) - [Official XLSR Wav2vec2 paper](https://arxiv.org/pdf/2006.13979.pdf) - [Hugging Face Blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) - [How does CTC (Connectionist Temporal Classification) work](https://distill.pub/2017/ctc/) It helps to have a good understanding of the following points: - How was XLSR-Wav2Vec2 pretrained? -> Feature vectors were masked and had to be predicted by the model; very similar in spirit to masked language model of BERT. - What parts of XLSR-Wav2Vec2 are responsible for what? What is the feature extractor part used for? -> extract feature vectors from the 1D raw audio waveform; What is the transformer part doing? -> mapping feature vectors to contextualized feature vectors; ... - What part of the model needs to be fine-tuned? -> The pretrained model **does not** include a language head to classify the contextualized features to letters. This is randomly initialized when loading the pretrained checkpoint and has to be fine-tuned. Also, note that the authors recommend to **not** further fine-tune the feature extractor. - What data was used to XLSR-Wav2Vec2? The checkpoint we will use for further fine-tuning was pretrained on **53** languages. - What languages are considered to be similar by XLSR-Wav2Vec2? In the official [XLSR Wav2Vec2 paper](https://arxiv.org/pdf/2006.13979.pdf), the authors show nicely which languages share a common contextualized latent space. It might be useful for you to extend your training data with data of other languages that are considered to be very similar by the model (or you). ## FAQ - Can a participant fine-tune models for more than one language? Yes! A participant can fine-tune models in as many languages she/he likes - Can a participant use extra data (apart from the common voice data)? Yes! All data except the official common voice `test data` can be used for training. If a participant wants to train a model on a language that is not part of Common Voice (which is very much encouraged!), the participant should make sure that some test data is held out to make sure the model is not overfitting. - Can we fine-tune for high-resource languages? Yes! While we do not really recommend people to fine-tune models in English since there are already so many fine-tuned speech recognition models in English. However, it is very much appreciated if participants want to fine-tune models in other "high-resource" languages, such as French, Spanish, or German. For such cases, one probably needs to train locally and apply might have to apply tricks such as lazy data loading (check the ["Lazy data loading"](#how-to-do-lazy-data-loading) section for more details).

{"language": "vi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice, infore_25h"], "metrics": ["wer"], "model-index": [{"name": "Cuong-Cong XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice vi", "type": "common_voice", "args": "vi"}, "metrics": [{"type": "wer", "value": 58.63, "name": "Test WER"}]}]}]}

CuongLD/wav2vec2-large-xlsr-vietnamese

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #vi #arxiv-2006.11477 #arxiv-2006.13979 #license-apache-2.0 #model-index #endpoints_compatible #region-us

# Wav2Vec2-Large-XLSR-53-Vietnamese Fine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, Infore_25h dataset (Password: BroughtToYouByInfoRe) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ## Evaluation The model can be evaluated as follows on the Vietnamese test data of Common Voice. Test Result: 58.63 % ## Training The Common Voice 'train', 'validation', and 'Infore_25h' datasets were used for training The script used for training can be found here =======================To here===============================> Your model in then available under *URL for everybody to use . ## How to evaluate my trained checkpoint Having uploaded your model, you should now evaluate your model in a final step. This should be as simple as copying the evaluation code of your model card into a python script and running it. Make sure to note the final result on the model card both under the YAML tags at the very top and below your evaluation code under "Test Results". ## Rules of training and evaluation In this section, we will quickly go over what data is allowed to be used as training data, what kind of data preprocessing is allowed be used, and how the model should be evaluated. To make it very simple regarding the first point: All data except the official common voice 'test' data set can be used as training data. For models trained in a language that is not included in Common Voice, the author of the model is responsible to leave a reasonable amount of data for evaluation. Second, the rules regarding the preprocessing are not that as straight-forward. It is allowed (and recommended) to normalize the data to only have lower-case characters. It is also allowed (and recommended) to remove typographical symbols and punctuation marks. A list of such symbols can *e.g.* be fonud here - however here we already must be careful. We should not remove a symbol that would change the meaning of the words, *e.g.* in English, we should not remove the single quotation mark ''' since it would change the meaning of the word '"it's"' to '"its"' which would then be incorrect. So the golden rule here is to not remove any characters that could change the meaning of a word into another word. This is not always obvious and should be given some consideration. As another example, it is fine to remove the "Hypen-minus" sign "'-'" since it doesn't change the meaninng of a word to another one. *E.g.* "'fine-tuning'" would be changed to "'finetuning'" which has still the same meaning. Since those choices are not always obvious when in doubt feel free to ask on Slack or even better post on the forum, as was done, *e.g.* here. ## Tips and tricks This section summarizes a couple of tips and tricks across various topics. It will continously be updated during the week. ### How to combine multiple datasets into one Check out this post. ### How to effectively preprocess the data ### How to do efficiently load datasets with limited ram and hard drive space Check out this post. ### How to do hyperparameter tuning ### How to preprocess and evaluate character based languages ## Further reading material It is recommended that take some time to read up on how Wav2vec2 works in theory. Getting a better understanding of the theory and the inner mechanisms of the model often helps when fine-tuning the model. However, if you don't like reading blog posts/papers, don't worry - it is by no means necessary to go through the theory to fine-tune Wav2Vec2 on your language of choice. If you are interested in learning more about the model though, here are a couple of resources that are important to better understand Wav2Vec2: - Facebook's Wav2Vec2 blog post - Official Wav2Vec2 paper - Official XLSR Wav2vec2 paper - Hugging Face Blog - How does CTC (Connectionist Temporal Classification) work It helps to have a good understanding of the following points: - How was XLSR-Wav2Vec2 pretrained? -> Feature vectors were masked and had to be predicted by the model; very similar in spirit to masked language model of BERT. - What parts of XLSR-Wav2Vec2 are responsible for what? What is the feature extractor part used for? -> extract feature vectors from the 1D raw audio waveform; What is the transformer part doing? -> mapping feature vectors to contextualized feature vectors; ... - What part of the model needs to be fine-tuned? -> The pretrained model does not include a language head to classify the contextualized features to letters. This is randomly initialized when loading the pretrained checkpoint and has to be fine-tuned. Also, note that the authors recommend to not further fine-tune the feature extractor. - What data was used to XLSR-Wav2Vec2? The checkpoint we will use for further fine-tuning was pretrained on 53 languages. - What languages are considered to be similar by XLSR-Wav2Vec2? In the official XLSR Wav2Vec2 paper, the authors show nicely which languages share a common contextualized latent space. It might be useful for you to extend your training data with data of other languages that are considered to be very similar by the model (or you). ## FAQ - Can a participant fine-tune models for more than one language? Yes! A participant can fine-tune models in as many languages she/he likes - Can a participant use extra data (apart from the common voice data)? Yes! All data except the official common voice 'test data' can be used for training. If a participant wants to train a model on a language that is not part of Common Voice (which is very much encouraged!), the participant should make sure that some test data is held out to make sure the model is not overfitting. - Can we fine-tune for high-resource languages? Yes! While we do not really recommend people to fine-tune models in English since there are already so many fine-tuned speech recognition models in English. However, it is very much appreciated if participants want to fine-tune models in other "high-resource" languages, such as French, Spanish, or German. For such cases, one probably needs to train locally and apply might have to apply tricks such as lazy data loading (check the "Lazy data loading" section for more details).

text-generation

# Sora DialoGPT Model

{"tags": ["conversational"]}

CurtisBowser/DialoGPT-medium-sora-two

2022-03-02T23:29:04+00:00

TAGS #pytorch #conversational #region-us

# Sora DialoGPT Model

text-generation

transformers

# Sora DialoGPT Model

{"tags": ["conversational"]}

CurtisBowser/DialoGPT-medium-sora

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space

# Sora DialoGPT Model

text-generation

transformers

# Sora DialoGPT Model

{"tags": ["conversational"]}

CurtisBowser/DialoGPT-small-sora

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Sora DialoGPT Model

text-generation

transformers

# Chandler Bot DialoGPT model

{"tags": ["conversational"]}

CyberMuffin/DialoGPT-small-ChandlerBot

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Chandler Bot DialoGPT model

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-base-discriminator-finetuned-cola This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6367 - Matthews Correlation: 0.6824 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4139 | 1.0 | 535 | 0.4137 | 0.6381 | | 0.2452 | 2.0 | 1070 | 0.4887 | 0.6504 | | 0.17 | 3.0 | 1605 | 0.5335 | 0.6757 | | 0.1135 | 4.0 | 2140 | 0.6367 | 0.6824 | | 0.0817 | 5.0 | 2675 | 0.6742 | 0.6755 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "electra-base-discriminator-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.6824089073723449, "name": "Matthews Correlation"}]}]}]}

D3xter1922/electra-base-discriminator-finetuned-cola

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

electra-base-discriminator-finetuned-cola ========================================= This model is a fine-tuned version of google/electra-base-discriminator on the glue dataset. It achieves the following results on the evaluation set: * Loss: 0.6367 * Matthews Correlation: 0.6824 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.10.0+cu111 * Datasets 1.17.0 * Tokenizers 0.10.3

text-generation

transformers

# Anakin Skywalker DialoGPT Model

{"tags": ["conversational"]}

DARKVIP3R/DialoGPT-medium-Anakin

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Anakin Skywalker DialoGPT Model

fill-mask

transformers

# bert-base-irish-cased-v1 [gaBERT](https://aclanthology.org/2022.lrec-1.511/) is a BERT-base model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. ## Model description Encoder-based Transformer to be used to obtain features for finetuning for downstream tasks in Irish. ## Intended uses & limitations Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations. ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Framework versions - Transformers 4.20.1 - TensorFlow 2.9.1 - Datasets 2.3.2 - Tokenizers 0.12.1 ### BibTeX entry and citation info If you use this model in your research, please consider citing our paper: ``` @inproceedings{barry-etal-2022-gabert, title = "ga{BERT} {---} an {I}rish Language Model", author = "Barry, James and Wagner, Joachim and Cassidy, Lauren and Cowap, Alan and Lynn, Teresa and Walsh, Abigail and {\'O} Meachair, M{\'\i}che{\'a}l J. and Foster, Jennifer", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.511", pages = "4774--4788", abstract = "The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.", } ```

{"tags": ["generated_from_keras_callback"], "widget": [{"text": "Ceolt\u00f3ir [MASK] ab ea Johnny Cash."}], "model-index": [{"name": "bert-base-irish-cased-v1", "results": []}]}

DCU-NLP/bert-base-irish-cased-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us

# bert-base-irish-cased-v1 gaBERT is a BERT-base model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. ## Model description Encoder-based Transformer to be used to obtain features for finetuning for downstream tasks in Irish. ## Intended uses & limitations Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations. ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Framework versions - Transformers 4.20.1 - TensorFlow 2.9.1 - Datasets 2.3.2 - Tokenizers 0.12.1 ### BibTeX entry and citation info If you use this model in your research, please consider citing our paper:

transformers

# gaELECTRA [gaELECTRA](https://aclanthology.org/2022.lrec-1.511/) is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model. ### Limitations and bias Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations. ### BibTeX entry and citation info If you use this model in your research, please consider citing our paper: ``` @inproceedings{barry-etal-2022-gabert, title = "ga{BERT} {---} an {I}rish Language Model", author = "Barry, James and Wagner, Joachim and Cassidy, Lauren and Cowap, Alan and Lynn, Teresa and Walsh, Abigail and {\'O} Meachair, M{\'\i}che{\'a}l J. and Foster, Jennifer", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.511", pages = "4774--4788", abstract = "The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.", } ```

{"language": ["ga"], "license": "apache-2.0", "tags": ["irish", "electra"], "widget": [{"text": "Ceolt\u00f3ir [MASK] ab ea Johnny Cash."}]}

DCU-NLP/electra-base-irish-cased-discriminator-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #electra #pretraining #irish #ga #license-apache-2.0 #endpoints_compatible #region-us

# gaELECTRA gaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model. ### Limitations and bias Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations. ### BibTeX entry and citation info If you use this model in your research, please consider citing our paper:

fill-mask

transformers

# gaELECTRA [gaELECTRA](https://aclanthology.org/2022.lrec-1.511/) is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model. ### Limitations and bias Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations. ### BibTeX entry and citation info If you use this model in your research, please consider citing our paper: ``` @inproceedings{barry-etal-2022-gabert, title = "ga{BERT} {---} an {I}rish Language Model", author = "Barry, James and Wagner, Joachim and Cassidy, Lauren and Cowap, Alan and Lynn, Teresa and Walsh, Abigail and {\'O} Meachair, M{\'\i}che{\'a}l J. and Foster, Jennifer", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.511", pages = "4774--4788", abstract = "The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.", } ```

{"language": ["ga"], "license": "apache-2.0", "tags": ["irish", "electra"], "widget": [{"text": "Ceolt\u00f3ir [MASK] ab ea Johnny Cash."}]}

DCU-NLP/electra-base-irish-cased-generator-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #electra #fill-mask #irish #ga #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us

# gaELECTRA gaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model. ### Limitations and bias Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations. ### BibTeX entry and citation info If you use this model in your research, please consider citing our paper:

fill-mask

transformers

# Danish BERT (uncased) model [BotXO.ai](https://www.botxo.ai/) developed this model. For data and training details see their [GitHub repository](https://github.com/botxo/nordic_bert). The original model was trained in TensorFlow then I converted it to Pytorch using [transformers-cli](https://huggingface.co/transformers/converting_tensorflow_models.html?highlight=cli). For TensorFlow version download here: https://www.dropbox.com/s/19cjaoqvv2jicq9/danish_bert_uncased_v2.zip?dl=1 ## Architecture ```python from transformers import AutoModelForPreTraining model = AutoModelForPreTraining.from_pretrained("DJSammy/bert-base-danish-uncased_BotXO,ai") params = list(model.named_parameters()) print('danish_bert_uncased_v2 has {:} different named parameters.\n'.format(len(params))) print('==== Embedding Layer ====\n') for p in params[0:5]: print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size())))) print('\n==== First Transformer ====\n') for p in params[5:21]: print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size())))) print('\n==== Last Transformer ====\n') for p in params[181:197]: print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size())))) print('\n==== Output Layer ====\n') for p in params[197:]: print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size())))) # danish_bert_uncased_v2 has 206 different named parameters. # ==== Embedding Layer ==== # bert.embeddings.word_embeddings.weight (32000, 768) # bert.embeddings.position_embeddings.weight (512, 768) # bert.embeddings.token_type_embeddings.weight (2, 768) # bert.embeddings.LayerNorm.weight (768,) # bert.embeddings.LayerNorm.bias (768,) # ==== First Transformer ==== # bert.encoder.layer.0.attention.self.query.weight (768, 768) # bert.encoder.layer.0.attention.self.query.bias (768,) # bert.encoder.layer.0.attention.self.key.weight (768, 768) # bert.encoder.layer.0.attention.self.key.bias (768,) # bert.encoder.layer.0.attention.self.value.weight (768, 768) # bert.encoder.layer.0.attention.self.value.bias (768,) # bert.encoder.layer.0.attention.output.dense.weight (768, 768) # bert.encoder.layer.0.attention.output.dense.bias (768,) # bert.encoder.layer.0.attention.output.LayerNorm.weight (768,) # bert.encoder.layer.0.attention.output.LayerNorm.bias (768,) # bert.encoder.layer.0.intermediate.dense.weight (3072, 768) # bert.encoder.layer.0.intermediate.dense.bias (3072,) # bert.encoder.layer.0.output.dense.weight (768, 3072) # bert.encoder.layer.0.output.dense.bias (768,) # bert.encoder.layer.0.output.LayerNorm.weight (768,) # bert.encoder.layer.0.output.LayerNorm.bias (768,) # ==== Last Transformer ==== # bert.encoder.layer.11.attention.self.query.weight (768, 768) # bert.encoder.layer.11.attention.self.query.bias (768,) # bert.encoder.layer.11.attention.self.key.weight (768, 768) # bert.encoder.layer.11.attention.self.key.bias (768,) # bert.encoder.layer.11.attention.self.value.weight (768, 768) # bert.encoder.layer.11.attention.self.value.bias (768,) # bert.encoder.layer.11.attention.output.dense.weight (768, 768) # bert.encoder.layer.11.attention.output.dense.bias (768,) # bert.encoder.layer.11.attention.output.LayerNorm.weight (768,) # bert.encoder.layer.11.attention.output.LayerNorm.bias (768,) # bert.encoder.layer.11.intermediate.dense.weight (3072, 768) # bert.encoder.layer.11.intermediate.dense.bias (3072,) # bert.encoder.layer.11.output.dense.weight (768, 3072) # bert.encoder.layer.11.output.dense.bias (768,) # bert.encoder.layer.11.output.LayerNorm.weight (768,) # bert.encoder.layer.11.output.LayerNorm.bias (768,) # ==== Output Layer ==== # bert.pooler.dense.weight (768, 768) # bert.pooler.dense.bias (768,) # cls.predictions.bias (32000,) # cls.predictions.transform.dense.weight (768, 768) # cls.predictions.transform.dense.bias (768,) # cls.predictions.transform.LayerNorm.weight (768,) # cls.predictions.transform.LayerNorm.bias (768,) # cls.seq_relationship.weight (2, 768) # cls.seq_relationship.bias (2,) ``` ## Example Pipeline ```python from transformers import pipeline unmasker = pipeline('fill-mask', model='DJSammy/bert-base-danish-uncased_BotXO,ai') unmasker('København er [MASK] i Danmark.') # Copenhagen is the [MASK] of Denmark. # => # [{'score': 0.788068950176239, # 'sequence': '[CLS] københavn er hovedstad i danmark. [SEP]', # 'token': 12610, # 'token_str': 'hovedstad'}, # {'score': 0.07606703042984009, # 'sequence': '[CLS] københavn er hovedstaden i danmark. [SEP]', # 'token': 8108, # 'token_str': 'hovedstaden'}, # {'score': 0.04299738258123398, # 'sequence': '[CLS] københavn er metropol i danmark. [SEP]', # 'token': 23305, # 'token_str': 'metropol'}, # {'score': 0.008163209073245525, # 'sequence': '[CLS] københavn er ikke i danmark. [SEP]', # 'token': 89, # 'token_str': 'ikke'}, # {'score': 0.006238455418497324, # 'sequence': '[CLS] københavn er ogsa i danmark. [SEP]', # 'token': 25253, # 'token_str': 'ogsa'}] ```

{"language": "da", "license": "cc-by-4.0", "tags": ["bert", "masked-lm"], "datasets": ["common_crawl", "wikipedia"], "pipeline_tag": "fill-mask", "widget": [{"text": "K\u00f8benhavn er [MASK] i Danmark."}]}

DJSammy/bert-base-danish-uncased_BotXO-ai

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #masked-lm #fill-mask #da #dataset-common_crawl #dataset-wikipedia #license-cc-by-4.0 #endpoints_compatible #region-us

# Danish BERT (uncased) model URL developed this model. For data and training details see their GitHub repository. The original model was trained in TensorFlow then I converted it to Pytorch using transformers-cli. For TensorFlow version download here: URL ## Architecture ## Example Pipeline

text-classification

transformers

** Human-Directed Sentiment Analysis in Arabic A supervised training procedure to classify human-directed-sentiment in a text. We define the human-directed-sentiment as the polarity of one user towards a second person who is involved with him in a discussion.

{}

DSI/human-directed-sentiment

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us

Human-Directed Sentiment Analysis in Arabic A supervised training procedure to classify human-directed-sentiment in a text. We define the human-directed-sentiment as the polarity of one user towards a second person who is involved with him in a discussion.

text-classification

transformers

# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT [Blog post »](https://people.cs.kuleuven.be/~pieter.delobelle/attitudes-towards-covid-19-measures/?utm_source=huggingface&utm_medium=social&utm_campaign=corona_tweets) · [paper »](http://arxiv.org/abs/2104.09947) This model can be used to determine if a tweet expresses support or not for a curfew. The model was trained on manually labeled tweets from Belgium in Dutch, French and English. We categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).  Models used in this paper are on HuggingFace: - https://huggingface.co/DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support - https://huggingface.co/DTAI-KULeuven/mbert-corona-tweets-belgium-topics

{"language": ["multilingual", "nl", "fr", "en"], "tags": ["Tweets", "Sentiment analysis"], "widget": [{"text": "I really wish I could leave my house after midnight, this makes no sense!"}]}

DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #text-classification #Tweets #Sentiment analysis #multilingual #nl #fr #en #arxiv-2104.09947 #autotrain_compatible #endpoints_compatible #region-us

# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT Blog post » · paper » This model can be used to determine if a tweet expresses support or not for a curfew. The model was trained on manually labeled tweets from Belgium in Dutch, French and English. We categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top). !URL Models used in this paper are on HuggingFace: - URL - URL

text-classification

transformers

# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT [Blog post »](https://people.cs.kuleuven.be/~pieter.delobelle/attitudes-towards-covid-19-measures/?utm_source=huggingface&utm_medium=social&utm_campaign=corona_tweets) · [paper »](http://arxiv.org/abs/2104.09947) We categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).  Models used in this paper are on HuggingFace: - https://huggingface.co/DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support - https://huggingface.co/DTAI-KULeuven/mbert-corona-tweets-belgium-topics

{"language": ["multilingual", "nl", "fr", "en"], "tags": ["Dutch", "French", "English", "Tweets", "Topic classification"], "widget": [{"text": "I really can't wait for this lockdown to be over and go back to waking up early."}]}

DTAI-KULeuven/mbert-corona-tweets-belgium-topics

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #text-classification #Dutch #French #English #Tweets #Topic classification #multilingual #nl #fr #en #arxiv-2104.09947 #autotrain_compatible #endpoints_compatible #region-us

# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT Blog post » · paper » We categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top). !URL Models used in this paper are on HuggingFace: - URL - URL

fill-mask

transformers

<p align="center"> <img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%"> </p> # About RobBERTje RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robbert). There are multiple models with different sizes and different training settings, which you can choose for your use-case. We are also continuously working on releasing better-performing models, so watch [the repository](http://github.com/iPieter/robbertje) for updates. # News - **February 21, 2022**: Our paper about RobBERTje has been published in [volume 11 of CLIN journal](https://www.clinjournal.org/clinj/article/view/131)! - **July 2, 2021**: Publicly released 4 RobBERTje models. - **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) for an oral presentation! # The models | Model | Description | Parameters | Training size | Huggingface id | |--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------| | Non-shuffled | Trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-non-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-non-shuffled) | | Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-shuffled) | | Merged (p=0.5) | Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-merged](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-merged) | | BORT | A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). | 46 M | 1 GB | this model | # Results ## Intrinsic results We calculated the _pseudo perplexity_ (PPPL) from [cite](), which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution. | Model | PPPL | |-------------------|-----------| | RobBERT (teacher) | 7.76 | | Non-shuffled | 12.95 | | Shuffled | 18.74 | | Merged (p=0.5) | 17.10 | | BORT | 26.44 | ## Extrinsic results We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a [Dutch NLI task named SICK-NL](https://arxiv.org/abs/2101.05716) was also released and we evaluated our models with it as well. | Model | DBRD | DIE-DAT | NER | POS |SICK-NL | |------------------|-----------|-----------|-----------|-----------|----------| | RobBERT (teacher)|94.4 | 99.2 |89.1 |96.4 | 84.2 | | Non-shuffled |90.2 | 98.4 |82.9 |95.5 | 83.4 | | Shuffled |92.5 | 98.2 |82.7 |95.6 | 83.4 | | Merged (p=0.5) |92.9 | 96.5 |81.8 |95.2 | 82.8 | | BORT |89.6 | 92.2 |79.7 |94.3 | 81.0 |

{"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje"], "datasets": ["oscar", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png", "widget": [{"text": "Hallo, ik ben RobBERTje, een gedistilleerd <mask> taalmodel van de KU Leuven."}]}

DTAI-KULeuven/robbertje-1-gb-bort

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us

 About RobBERTje =============== RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case. We are also continuously working on releasing better-performing models, so watch the repository for updates. News ==== * February 21, 2022: Our paper about RobBERTje has been published in volume 11 of CLIN journal! * July 2, 2021: Publicly released 4 RobBERTje models. * May 12, 2021: RobBERTje was accepted at CLIN31 for an oral presentation! The models ========== Results ======= Intrinsic results ----------------- We calculated the *pseudo perplexity* (PPPL) from cite, which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution. Extrinsic results ----------------- We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a Dutch NLI task named SICK-NL was also released and we evaluated our models with it as well.

fill-mask

transformers

<p align="center"> <img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%"> </p> # About RobBERTje RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robbert). There are multiple models with different sizes and different training settings, which you can choose for your use-case. We are also continuously working on releasing better-performing models, so watch [the repository](http://github.com/iPieter/robbertje) for updates. # News - **February 21, 2022**: Our paper about RobBERTje has been published in [volume 11 of CLIN journal](https://www.clinjournal.org/clinj/article/view/131)! - **July 2, 2021**: Publicly released 4 RobBERTje models. - **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) for an oral presentation! # The models | Model | Description | Parameters | Training size | Huggingface id | |--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------| | Non-shuffled | Trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-non-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-non-shuffled) | | Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-shuffled) | | Merged (p=0.5) | Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. | 74 M | 1 GB | this model | | BORT | A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). | 46 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-bort](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-bort) | # Results ## Intrinsic results We calculated the _pseudo perplexity_ (PPPL) from [cite](), which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution. | Model | PPPL | |-------------------|-----------| | RobBERT (teacher) | 7.76 | | Non-shuffled | 12.95 | | Shuffled | 18.74 | | Merged (p=0.5) | 17.10 | | BORT | 26.44 | ## Extrinsic results We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a [Dutch NLI task named SICK-NL](https://arxiv.org/abs/2101.05716) was also released and we evaluated our models with it as well. | Model | DBRD | DIE-DAT | NER | POS |SICK-NL | |------------------|-----------|-----------|-----------|-----------|----------| | RobBERT (teacher)|94.4 | 99.2 |89.1 |96.4 | 84.2 | | Non-shuffled |90.2 | 98.4 |82.9 |95.5 | 83.4 | | Shuffled |92.5 | 98.2 |82.7 |95.6 | 83.4 | | Merged (p=0.5) |92.9 | 96.5 |81.8 |95.2 | 82.8 | | BORT |89.6 | 92.2 |79.7 |94.3 | 81.0 |

{"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje"], "datasets": ["oscar", "oscar (NL)", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png", "widget": [{"text": "Hallo, ik ben RobBERTje, een gedistilleerd <mask> taalmodel van de KU Leuven."}]}

DTAI-KULeuven/robbertje-1-gb-merged

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us

 About RobBERTje =============== RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case. We are also continuously working on releasing better-performing models, so watch the repository for updates. News ==== * February 21, 2022: Our paper about RobBERTje has been published in volume 11 of CLIN journal! * July 2, 2021: Publicly released 4 RobBERTje models. * May 12, 2021: RobBERTje was accepted at CLIN31 for an oral presentation! The models ========== Results ======= Intrinsic results ----------------- We calculated the *pseudo perplexity* (PPPL) from cite, which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution. Extrinsic results ----------------- We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a Dutch NLI task named SICK-NL was also released and we evaluated our models with it as well.

fill-mask

transformers

<p align="center"> <img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%"> </p> # About RobBERTje RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robbert). There are multiple models with different sizes and different training settings, which you can choose for your use-case. We are also continuously working on releasing better-performing models, so watch [the repository](http://github.com/iPieter/robbertje) for updates. # News - **February 21, 2022**: Our paper about RobBERTje has been published in [volume 11 of CLIN journal](https://www.clinjournal.org/clinj/article/view/131)! - **July 2, 2021**: Publicly released 4 RobBERTje models. - **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) for an oral presentation! # The models | Model | Description | Parameters | Training size | Huggingface id | |--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------| | Non-shuffled | Trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. | 74 M | 1 GB | this model | | Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-shuffled) | | Merged (p=0.5) | Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-merged](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-merged) | | BORT | A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). | 46 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-bort](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-bort) | # Results ## Intrinsic results We calculated the _pseudo perplexity_ (PPPL) from [cite](), which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution. | Model | PPPL | |-------------------|-----------| | RobBERT (teacher) | 7.76 | | Non-shuffled | 12.95 | | Shuffled | 18.74 | | Merged (p=0.5) | 17.10 | | BORT | 26.44 | ## Extrinsic results We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a [Dutch NLI task named SICK-NL](https://arxiv.org/abs/2101.05716) was also released and we evaluated our models with it as well. | Model | DBRD | DIE-DAT | NER | POS |SICK-NL | |------------------|-----------|-----------|-----------|-----------|----------| | RobBERT (teacher)|94.4 | 99.2 |89.1 |96.4 | 84.2 | | Non-shuffled |90.2 | 98.4 |82.9 |95.5 | 83.4 | | Shuffled |92.5 | 98.2 |82.7 |95.6 | 83.4 | | Merged (p=0.5) |92.9 | 96.5 |81.8 |95.2 | 82.8 | | BORT |89.6 | 92.2 |79.7 |94.3 | 81.0 |

{"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje"], "datasets": ["oscar", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png", "widget": [{"text": "Hallo, ik ben RobBERTje, een gedistilleerd <mask> taalmodel van de KU Leuven."}]}

DTAI-KULeuven/robbertje-1-gb-non-shuffled

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us

 About RobBERTje =============== RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case. We are also continuously working on releasing better-performing models, so watch the repository for updates. News ==== * February 21, 2022: Our paper about RobBERTje has been published in volume 11 of CLIN journal! * July 2, 2021: Publicly released 4 RobBERTje models. * May 12, 2021: RobBERTje was accepted at CLIN31 for an oral presentation! The models ========== Results ======= Intrinsic results ----------------- We calculated the *pseudo perplexity* (PPPL) from cite, which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution. Extrinsic results ----------------- We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a Dutch NLI task named SICK-NL was also released and we evaluated our models with it as well.

fill-mask

transformers

<p align="center"> <img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%"> </p> # About RobBERTje RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robbert). There are multiple models with different sizes and different training settings, which you can choose for your use-case. We are also continuously working on releasing better-performing models, so watch [the repository](http://github.com/iPieter/robbertje) for updates. # News - **February 21, 2022**: Our paper about RobBERTje has been published in [volume 11 of CLIN journal](https://www.clinjournal.org/clinj/article/view/131)! - **July 2, 2021**: Publicly released 4 RobBERTje models. - **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) for an oral presentation! # The models | Model | Description | Parameters | Training size | Huggingface id | |--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------| | Non-shuffled | Trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-non-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-non-shuffled) | | Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | this model | | Merged (p=0.5) | Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-merged](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-merged) | | BORT | A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). | 46 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-bort](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-bort) | # Results ## Intrinsic results We calculated the _pseudo perplexity_ (PPPL) from [cite](), which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution. | Model | PPPL | |-------------------|-----------| | RobBERT (teacher) | 7.76 | | Non-shuffled | 12.95 | | Shuffled | 18.74 | | Merged (p=0.5) | 17.10 | | BORT | 26.44 | ## Extrinsic results We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a [Dutch NLI task named SICK-NL](https://arxiv.org/abs/2101.05716) was also released and we evaluated our models with it as well. | Model | DBRD | DIE-DAT | NER | POS |SICK-NL | |------------------|-----------|-----------|-----------|-----------|----------| | RobBERT (teacher)|94.4 | 99.2 |89.1 |96.4 | 84.2 | | Non-shuffled |90.2 | 98.4 |82.9 |95.5 | 83.4 | | Shuffled |92.5 | 98.2 |82.7 |95.6 | 83.4 | | Merged (p=0.5) |92.9 | 96.5 |81.8 |95.2 | 82.8 | | BORT |89.6 | 92.2 |79.7 |94.3 | 81.0 |

{"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje"], "datasets": ["oscar", "oscar (NL)", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png", "widget": [{"text": "Hallo, ik ben RobBERTje, een gedistilleerd <mask> taalmodel van de KU Leuven."}]}

DTAI-KULeuven/robbertje-1-gb-shuffled

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us

 About RobBERTje =============== RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case. We are also continuously working on releasing better-performing models, so watch the repository for updates. News ==== * February 21, 2022: Our paper about RobBERTje has been published in volume 11 of CLIN journal! * July 2, 2021: Publicly released 4 RobBERTje models. * May 12, 2021: RobBERTje was accepted at CLIN31 for an oral presentation! The models ========== Results ======= Intrinsic results ----------------- We calculated the *pseudo perplexity* (PPPL) from cite, which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution. Extrinsic results ----------------- We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a Dutch NLI task named SICK-NL was also released and we evaluated our models with it as well.

text-classification

transformers

# Danish BERT for emotion detection The BERT Emotion model detects whether a Danish text is emotional or not. It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-emotion) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-binary-emotion-classification-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-binary-emotion-classification-base") ``` ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

{"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Der er et tr\u00e6 i haven."}]}

alexandrainst/da-binary-emotion-classification-base

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Danish BERT for emotion detection The BERT Emotion model detects whether a Danish text is emotional or not. It is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. See the DaNLP documentation for more details. Here is how to use the model: ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

text-classification

transformers

# Danish BERT for emotion classification The BERT Emotion model classifies a Danish text in one of the following class: * Glæde/Sindsro * Tillid/Accept * Forventning/Interrese * Overasket/Målløs * Vrede/Irritation * Foragt/Modvilje * Sorg/trist * Frygt/Bekymret It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data. This model should be used after detecting whether the text contains emotion or not, using the binary [BERT Emotion model](https://huggingface.co/alexandrainst/da-binary-emotion-classification-base). See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-emotion) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-emotion-classification-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-emotion-classification-base") ``` ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

{"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Jeg ejer en r\u00f8d bil og det er en god bil."}]}

alexandrainst/da-emotion-classification-base

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us

# Danish BERT for emotion classification The BERT Emotion model classifies a Danish text in one of the following class: * Glæde/Sindsro * Tillid/Accept * Forventning/Interrese * Overasket/Målløs * Vrede/Irritation * Foragt/Modvilje * Sorg/trist * Frygt/Bekymret It is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. This model should be used after detecting whether the text contains emotion or not, using the binary BERT Emotion model. See the DaNLP documentation for more details. Here is how to use the model: ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

text-classification

transformers

# Danish BERT for hate speech classification The BERT HateSpeech model classifies offensive Danish text into 4 categories: * `Særlig opmærksomhed` (special attention, e.g. threat) * `Personangreb` (personal attack) * `Sprogbrug` (offensive language) * `Spam & indhold` (spam) This model is intended to be used after the [BERT HateSpeech detection model](https://huggingface.co/alexandrainst/da-hatespeech-detection-base). It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/hatespeech.html#bertdr) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-hatespeech-classification-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-hatespeech-classification-base") ``` ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

{"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Senile gamle idiot"}]}

alexandrainst/da-hatespeech-classification-base

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Danish BERT for hate speech classification The BERT HateSpeech model classifies offensive Danish text into 4 categories: * 'Særlig opmærksomhed' (special attention, e.g. threat) * 'Personangreb' (personal attack) * 'Sprogbrug' (offensive language) * 'Spam & indhold' (spam) This model is intended to be used after the BERT HateSpeech detection model. It is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. See the DaNLP documentation for more details. Here is how to use the model: ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

text-classification

transformers

# Danish BERT for hate speech (offensive language) detection The BERT HateSpeech model detects whether a Danish text is offensive or not. It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/hatespeech.html#bertdr) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-hatespeech-detection-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-hatespeech-detection-base") ``` ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

{"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Senile gamle idiot"}]}

alexandrainst/da-hatespeech-detection-base

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Danish BERT for hate speech (offensive language) detection The BERT HateSpeech model detects whether a Danish text is offensive or not. It is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. See the DaNLP documentation for more details. Here is how to use the model: ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

token-classification

transformers

# BERT fine-tuned for Named Entity Recognition in Danish The model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC]. The pretrained language model used for fine-tuning is the [Danish BERT](https://github.com/certainlyio/nordic_bert) by BotXO. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/ner.html#bert) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForTokenClassification model = BertForTokenClassification.from_pretrained("alexandrainst/da-ner-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-ner-base") ``` ## Training Data The model has been trained on the [DaNE](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dane).

{"language": ["da"], "license": "apache-2.0", "datasets": ["dane"], "widget": [{"text": "Jens Peter Hansen kommer fra Danmark"}]}

alexandrainst/da-ner-base

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #bert #token-classification #da #dataset-dane #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# BERT fine-tuned for Named Entity Recognition in Danish The model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC]. The pretrained language model used for fine-tuning is the Danish BERT by BotXO. See the DaNLP documentation for more details. Here is how to use the model: ## Training Data The model has been trained on the DaNE.

text-classification

transformers

# Model Card for Danish BERT Danish BERT Tone for sentiment polarity detection # Model Details ## Model Description The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been finetuned on the pretrained Danish BERT model by BotXO. - **Developed by:** DaNLP - **Shared by [Optional]:** Hugging Face - **Model type:** Text Classification - **Language(s) (NLP):** Danish (da) - **License:** cc-by-sa-4.0 - **Related Models:** More information needed - **Parent Model:** BERT - **Resources for more information:** - [GitHub Repo](https://github.com/certainlyio/nordic_bert) - [Associated Documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-tone) # Uses ## Direct Use This model can be used for text classification ## Downstream Use [Optional] More information needed. ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data The data used for training come from the [Twitter Sentiment](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent) and [EuroParl sentiment 2](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2) datasets. ## Training Procedure ### Preprocessing It has been finetuned on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO. ### Speeds, Sizes, Times More information needed. # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed. ### Factors ### Metrics F1 ## Results More information needed. # Model Examination More information needed. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed. - **Hours used:** More information needed. - **Cloud Provider:** More information needed. - **Compute Region:** More information needed. - **Carbon Emitted:** More information needed. # Technical Specifications [optional] ## Model Architecture and Objective More information needed. ## Compute Infrastructure More information needed. ### Hardware More information needed. ### Software More information needed. # Citation **BibTeX:** More information needed. **APA:** More information needed. # Glossary [optional] More information needed. # More Information [optional] More information needed. # Model Card Authors [optional] DaNLP in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed. # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-sentiment-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-sentiment-base") ``` </details>

{"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Det er super godt"}]}

alexandrainst/da-sentiment-base

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #safetensors #bert #text-classification #da #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Model Card for Danish BERT Danish BERT Tone for sentiment polarity detection # Model Details ## Model Description The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been finetuned on the pretrained Danish BERT model by BotXO. - Developed by: DaNLP - Shared by [Optional]: Hugging Face - Model type: Text Classification - Language(s) (NLP): Danish (da) - License: cc-by-sa-4.0 - Related Models: More information needed - Parent Model: BERT - Resources for more information: - GitHub Repo - Associated Documentation # Uses ## Direct Use This model can be used for text classification ## Downstream Use [Optional] More information needed. ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data The data used for training come from the Twitter Sentiment and EuroParl sentiment 2 datasets. ## Training Procedure ### Preprocessing It has been finetuned on the pretrained Danish BERT model by BotXO. ### Speeds, Sizes, Times More information needed. # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed. ### Factors ### Metrics F1 ## Results More information needed. # Model Examination More information needed. # Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: More information needed. - Hours used: More information needed. - Cloud Provider: More information needed. - Compute Region: More information needed. - Carbon Emitted: More information needed. # Technical Specifications [optional] ## Model Architecture and Objective More information needed. ## Compute Infrastructure More information needed. ### Hardware More information needed. ### Software More information needed. BibTeX: More information needed. APA: More information needed. # Glossary [optional] More information needed. # More Information [optional] More information needed. # Model Card Authors [optional] DaNLP in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed. # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> </details>

text-classification

transformers

# Danish BERT Tone for the detection of subjectivity/objectivity The BERT Tone model detects whether a text (in Danish) is subjective or objective. The model is based on the finetuning of the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-tone) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-subjectivivity-classification-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-subjectivivity-classification-base") ``` ## Training data The data used for training come from the [Twitter Sentiment](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent) and [EuroParl sentiment 2](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2) datasets.

{"language": ["da"], "license": "apache-2.0", "datasets": ["DDSC/twitter-sent", "DDSC/europarl"], "widget": [{"text": "Jeg tror alligvel, det bliver godt"}]}

alexandrainst/da-subjectivivity-classification-base

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #safetensors #bert #text-classification #da #dataset-DDSC/twitter-sent #dataset-DDSC/europarl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Danish BERT Tone for the detection of subjectivity/objectivity The BERT Tone model detects whether a text (in Danish) is subjective or objective. The model is based on the finetuning of the pretrained Danish BERT model by BotXO. See the DaNLP documentation for more details. Here is how to use the model: ## Training data The data used for training come from the Twitter Sentiment and EuroParl sentiment 2 datasets.

text-classification

transformers

# Danish ELECTRA for hate speech (offensive language) detection The ELECTRA Offensive model detects whether a Danish text is offensive or not. It is based on the pretrained [Danish Ælæctra](Maltehb/aelaectra-danish-electra-small-cased) model. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/hatespeech.html#electra) for more details. Here is how to use the model: ```python from transformers import ElectraTokenizer, ElectraForSequenceClassification model = ElectraForSequenceClassification.from_pretrained("alexandrainst/da-hatespeech-detection-small") tokenizer = ElectraTokenizer.from_pretrained("alexandrainst/da-hatespeech-detection-small") ``` ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

{"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Senile gamle idiot"}]}

alexandrainst/da-hatespeech-detection-small

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #electra #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Danish ELECTRA for hate speech (offensive language) detection The ELECTRA Offensive model detects whether a Danish text is offensive or not. It is based on the pretrained Danish Ælæctra model. See the DaNLP documentation for more details. Here is how to use the model: ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

text-classification

transformers

# XLM-Roberta fine-tuned for Named Entity Disambiguation Given a sentence and a knowledge graph context, the model detects whether a specific entity (represented by the knowledge graph context) is mentioned in the sentence (binary classification). The base language model used is the [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). Here is how to use the model: ```python from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification model = XLMRobertaForSequenceClassification.from_pretrained("alexandrainst/da-ned-base") tokenizer = XLMRobertaTokenizer.from_pretrained("alexandrainst/da-ned-base") ``` The tokenizer takes 2 strings has input: the sentence and the knowledge graph (KG) context. Here is an example: ```python sentence = "Karen Blixen vendte tilbage til Danmark, hvor hun boede resten af sit liv på Rungstedlund, som hun arvede efter sin mor i 1939" kg_context = "udmærkelser modtaget Kritikerprisen udmærkelser modtaget Tagea Brandts Rejselegat udmærkelser modtaget Ingenio et arti udmærkelser modtaget Holbergmedaljen udmærkelser modtaget De Gyldne Laurbær mor Ingeborg Dinesen ægtefælle Bror von Blixen-Finecke køn kvinde Commons-kategori Karen Blixen LCAuth no95003722 VIAF 90663542 VIAF 121643918 GND-identifikator 118637878 ISNI 0000 0001 2096 6265 ISNI 0000 0003 6863 4408 ISNI 0000 0001 1891 0457 fødested Rungstedlund fødested Rungsted dødssted Rungstedlund dødssted København statsborgerskab Danmark NDL-nummer 00433530 dødsdato +1962-09-07T00:00:00Z dødsdato +1962-01-01T00:00:00Z fødselsdato +1885-04-17T00:00:00Z fødselsdato +1885-01-01T00:00:00Z AUT NKC jn20000600905 AUT NKC jo2015880827 AUT NKC xx0196181 emnets hovedkategori Kategori:Karen Blixen tilfælde af menneske billede Karen Blixen cropped from larger original.jpg IMDb-identifikationsnummer nm0227598 Freebase-ID /m/04ymd8w BNF 118857710 beskæftigelse skribent beskæftigelse selvbiograf beskæftigelse novelleforfatter ..." ``` A KG context, for a specific entity, can be generated from its Wikidata page. In the previous example, the KG context is a string representation of the Wikidata page of [Karen Blixen (QID=Q182804)](https://www.wikidata.org/wiki/Q182804). See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/ned.html#xlmr) for more details about how to generate a KG context. ## Training Data The model has been trained on the [DaNED](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#daned) and [DaWikiNED](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dawikined) datasets.

{"language": ["da"], "license": "apache-2.0"}

alexandrainst/da-ned-base

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #safetensors #xlm-roberta #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# XLM-Roberta fine-tuned for Named Entity Disambiguation Given a sentence and a knowledge graph context, the model detects whether a specific entity (represented by the knowledge graph context) is mentioned in the sentence (binary classification). The base language model used is the xlm-roberta-base. Here is how to use the model: The tokenizer takes 2 strings has input: the sentence and the knowledge graph (KG) context. Here is an example: A KG context, for a specific entity, can be generated from its Wikidata page. In the previous example, the KG context is a string representation of the Wikidata page of Karen Blixen (QID=Q182804). See the DaNLP documentation for more details about how to generate a KG context. ## Training Data The model has been trained on the DaNED and DaWikiNED datasets.

text-generation

transformers

#Saitama DialoGPT model

{"tags": ["conversational"]}

Daivakai/DialoGPT-small-saitama

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#Saitama DialoGPT model

text-classification

transformers

# scientific-challenges-and-directions We present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. At a high level, the _challenges_ and _directions_ are defined as follows: * **Challenge**: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap. * **Research direction**: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration. * This model here is described in our paper: [A Search Engine for Discovery of Scientific Challenges and Directions](https://arxiv.org/abs/2108.13751) (though we've upgraded the infrastructure since the paper was released - there are slight differences in the results). * Our dataset can be found [here](https://huggingface.co/datasets/DanL/scientific-challenges-and-directions-dataset). * Please cite our paper if you use our datasets or models in your project. See the [BibTeX](#citation). * Feel free to [email us](#contact-us). * Also, check out [our search engine](https://challenges.apps.allenai.org/), as an example application. ## Model description This model is a fine-tuned version of [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [scientific-challenges-and-directions-dataset](https://huggingface.co/datasets/DanL/scientific-challenges-and-directions-dataset), designed for multi-label text classification. ## Training and evaluation data The scientific-challenges-and-directions model is trained based on a dataset that is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the CORD-19 corpus, labeled for classification of challenges and directions by expert annotators with biomedical and bioNLP backgrounds. For full details on the train/test/split of the data see section 3.1 in our [paper](https://arxiv.org/abs/2108.13751) ## Example notebook We include an example notebook that uses the model for inference in our [repo](https://github.com/Dan-La/scientific-challenges-and-directions). See `Inference_Notebook.ipynb`. A training notebook is also included. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning rate: 2e-05 - train batch size: 8 - eval batch size: 4 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr scheduler type: linear - lr scheduler warmup steps: 500 - num epochs: 30 ### Training results The achieves the following results on the test set: - Precision Challenge: 0.768719 - Recall Challenge: 0.780405 - F1 Challenge: 0.774518 - Precision Direction: 0.758112 - Recall Direction: 0.774096 - F1 Direction: 0.766021 - Precision (micro avg. on both labels): 0.764894 - Recall (micro avg. on both labels): 0.778139 - F1 (micro avg. on both labels): 0.771459 ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3 ## Citation If using our dataset and models, please cite: ``` @misc{lahav2021search, title={A Search Engine for Discovery of Scientific Challenges and Directions}, author={Dan Lahav and Jon Saad Falcon and Bailey Kuehl and Sophie Johnson and Sravanthi Parasa and Noam Shomron and Duen Horng Chau and Diyi Yang and Eric Horvitz and Daniel S. Weld and Tom Hope}, year={2021}, eprint={2108.13751}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Contact us Please don't hesitate to reach out. **Email:** `lahav@mail.tau.ac.il`,`tomh@allenai.org`.

{"language": ["en"], "tags": ["generated_from_trainer", "text-classification"], "datasets": ["DanL/scientific-challenges-and-directions-dataset"], "metrics": ["precision", "recall", "f1"], "widget": [{"text": "severe atypical cases of pneumonia emerged and quickly spread worldwide.", "example_title": "challenge"}, {"text": "we speculate that studying IL-6 will be beneficial.", "example_title": "direction"}, {"text": "in future studies, both PRRs should be tested as the cause for multiple deaths.", "example_title": "both"}, {"text": "IbMADS1-transformed potatoes exhibited tuber morphogenesis in the fibrous roots.", "example_title": "neither"}], "model-index": [{"name": "scientific-challenges-and-directions", "results": []}]}

DanL/scientific-challenges-and-directions

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-DanL/scientific-challenges-and-directions-dataset #arxiv-2108.13751 #autotrain_compatible #endpoints_compatible #region-us

# scientific-challenges-and-directions We present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. At a high level, the _challenges_ and _directions_ are defined as follows: * Challenge: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap. * Research direction: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration. * This model here is described in our paper: A Search Engine for Discovery of Scientific Challenges and Directions (though we've upgraded the infrastructure since the paper was released - there are slight differences in the results). * Our dataset can be found here. * Please cite our paper if you use our datasets or models in your project. See the BibTeX. * Feel free to email us. * Also, check out our search engine, as an example application. ## Model description This model is a fine-tuned version of PubMedBERT on the scientific-challenges-and-directions-dataset, designed for multi-label text classification. ## Training and evaluation data The scientific-challenges-and-directions model is trained based on a dataset that is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the CORD-19 corpus, labeled for classification of challenges and directions by expert annotators with biomedical and bioNLP backgrounds. For full details on the train/test/split of the data see section 3.1 in our paper ## Example notebook We include an example notebook that uses the model for inference in our repo. See 'Inference_Notebook.ipynb'. A training notebook is also included. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning rate: 2e-05 - train batch size: 8 - eval batch size: 4 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr scheduler type: linear - lr scheduler warmup steps: 500 - num epochs: 30 ### Training results The achieves the following results on the test set: - Precision Challenge: 0.768719 - Recall Challenge: 0.780405 - F1 Challenge: 0.774518 - Precision Direction: 0.758112 - Recall Direction: 0.774096 - F1 Direction: 0.766021 - Precision (micro avg. on both labels): 0.764894 - Recall (micro avg. on both labels): 0.778139 - F1 (micro avg. on both labels): 0.771459 ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3 If using our dataset and models, please cite: ## Contact us Please don't hesitate to reach out. Email: 'lahav@URL','tomh@URL'.

text-classification

transformers

Fine-tuned CovidBERT on Med-Marco Dataset for passage ranking # CovidBERT-MedNLI This is the model **CovidBERT** trained by DeepSet on AllenAI's [CORD19 Dataset](https://pages.semanticscholar.org/coronavirus-research) of scientific articles about coronaviruses. The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [1] using the **average pooling strategy** and a **softmax loss**. It is further fine-tuned Med-Marco Dataset. MacAvaney et.al in their [paper](https://arxiv.org/abs/2010.05987) titled “SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search” used MedSyn a lexicon of layperson and expert terminology for various medical conditions to filter for medical questions. One can also replace this by UMLs ontologies but the beauty of MedSyn is that the terms are more general human conversation lingo and not terms based on scientific literature. Parameter details for the original training on CORD-19 are available on [DeepSet's MLFlow](https://public-mlflow.deepset.ai/#/experiments/2/runs/ba27d00c30044ef6a33b1d307b4a6cba) **Base model**: `deepset/covid_bert_base` from HuggingFace's `AutoModel`.

{}

Darkrider/covidbert_medmarco

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #text-classification #arxiv-2010.05987 #autotrain_compatible #endpoints_compatible #region-us

Fine-tuned CovidBERT on Med-Marco Dataset for passage ranking # CovidBERT-MedNLI This is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses. The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss. It is further fine-tuned Med-Marco Dataset. MacAvaney URL in their paper titled “SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search” used MedSyn a lexicon of layperson and expert terminology for various medical conditions to filter for medical questions. One can also replace this by UMLs ontologies but the beauty of MedSyn is that the terms are more general human conversation lingo and not terms based on scientific literature. Parameter details for the original training on CORD-19 are available on DeepSet's MLFlow Base model: 'deepset/covid_bert_base' from HuggingFace's 'AutoModel'.

transformers

# CovidBERT-MedNLI This is the model **CovidBERT** trained by DeepSet on AllenAI's [CORD19 Dataset](https://pages.semanticscholar.org/coronavirus-research) of scientific articles about coronaviruses. The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [1] using the **average pooling strategy** and a **softmax loss**. It is further fine-tuned on both MedNLI datasets available at Physionet. [ACL-BIONLP 2019](https://physionet.org/content/mednli-bionlp19/1.0.1/) [MedNLI from MIMIC](https://physionet.org/content/mednli/1.0.0/) Parameter details for the original training on CORD-19 are available on [DeepSet's MLFlow](https://public-mlflow.deepset.ai/#/experiments/2/runs/ba27d00c30044ef6a33b1d307b4a6cba) **Base model**: `deepset/covid_bert_base` from HuggingFace's `AutoModel`.

{}

Darkrider/covidbert_mednli

2022-03-02T23:29:04+00:00

TAGS #transformers #endpoints_compatible #region-us

# CovidBERT-MedNLI This is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses. The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss. It is further fine-tuned on both MedNLI datasets available at Physionet. ACL-BIONLP 2019 MedNLI from MIMIC Parameter details for the original training on CORD-19 are available on DeepSet's MLFlow Base model: 'deepset/covid_bert_base' from HuggingFace's 'AutoModel'.

fill-mask

transformers

# Marathi DistilBERT ## Model description This model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences. ``` DISCLAIMER This model has not been thoroughly tested and may contain biased opinions or inappropriate language. User discretion is advised ``` ## Training data The training data has been extracted from a variety of sources, mainly including: 1. Oscar Corpus 2. Marathi Newspapers 3. Marathi storybooks and articles The data is cleaned by removing all languages other than Marathi, while preserving common punctuations ## Training procedure The model is trained from scratch using an Adam optimizer with a learning rate of 1e-4 and default β1 and β2 values of 0.9 and 0.999 respectively with a total batch size of 256 on a v3-8 TPU and mask probability of 15%. ## Example ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="DarshanDeshpande/marathi-distilbert", tokenizer="DarshanDeshpande/marathi-distilbert", ) fill_mask("हा खरोखर चांगला [MASK] आहे.") ``` ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <h3>Authors </h3> <h5>1. Darshan Deshpande: <a href="https://github.com/DarshanDeshpande">GitHub</a>, <a href="https://www.linkedin.com/in/darshan-deshpande/">LinkedIn</a><h5> <h5>2. Harshavardhan Abichandani: <a href="https://github.com/Baras64">GitHub</a>, <a href="http://​www.linkedin.com/in/harsh-abhi">LinkedIn</a><h5>

{"language": ["mr"], "license": "apache-2.0", "tags": ["fill-mask"], "datasets": ["Oscar Corpus, News, Stories"], "widget": [{"text": "\u0939\u093e \u0916\u0930\u094b\u0916\u0930 \u091a\u093e\u0902\u0917\u0932\u093e [MASK] \u0906\u0939\u0947."}]}

DarshanDeshpande/marathi-distilbert

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #distilbert #fill-mask #mr #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Marathi DistilBERT ## Model description This model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences. ## Training data The training data has been extracted from a variety of sources, mainly including: 1. Oscar Corpus 2. Marathi Newspapers 3. Marathi storybooks and articles The data is cleaned by removing all languages other than Marathi, while preserving common punctuations ## Training procedure The model is trained from scratch using an Adam optimizer with a learning rate of 1e-4 and default β1 and β2 values of 0.9 and 0.999 respectively with a total batch size of 256 on a v3-8 TPU and mask probability of 15%. ## Example ### BibTeX entry and citation info <h3>Authors </h3> <h5>1. Darshan Deshpande: <a href="URL <a href="URL <h5>2. Harshavardhan Abichandani: <a href="URL <a href="http://​URL

fill-mask

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-retrained_ru_covid This model is a fine-tuned version of [blinoff/roberta-base-russian-v0](https://huggingface.co/blinoff/roberta-base-russian-v0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8518 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-retrained_ru_covid", "results": []}]}

Daryaflp/roberta-retrained_ru_covid

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

# roberta-retrained_ru_covid This model is a fine-tuned version of blinoff/roberta-base-russian-v0 on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8518 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

transformers

TinyBERT: Distilling BERT for Natural Language Understanding ======== **This model is a copy of [this model repository](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) from Huawei Noah at the specific commit `34707a33cd59a94ecde241ac209bf35103691b43`.** TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand. For more details about the techniques of TinyBERT, refer to our paper: [TinyBERT: Distilling BERT for Natural Language Understanding](https://arxiv.org/abs/1909.10351) Citation ======== If you find TinyBERT useful in your research, please cite the following paper: ``` @article{jiao2019tinybert, title={Tinybert: Distilling bert for natural language understanding}, author={Jiao, Xiaoqi and Yin, Yichun and Shang, Lifeng and Jiang, Xin and Chen, Xiao and Li, Linlin and Wang, Fang and Liu, Qun}, journal={arXiv preprint arXiv:1909.10351}, year={2019} } ```

{}

DataikuNLP/TinyBERT_General_4L_312D

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #arxiv-1909.10351 #endpoints_compatible #region-us

TinyBERT: Distilling BERT for Natural Language Understanding ======== This model is a copy of this model repository from Huawei Noah at the specific commit '34707a33cd59a94ecde241ac209bf35103691b43'. TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand. For more details about the techniques of TinyBERT, refer to our paper: TinyBERT: Distilling BERT for Natural Language Understanding Citation ======== If you find TinyBERT useful in your research, please cite the following paper:

sentence-similarity

sentence-transformers

# average_word_embeddings_glove.6B.300d **This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d) from sentence-transformers at the specific commit `5d2b7d1c127036ae98b9d487eca4d48744edc709`.** This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/average_word_embeddings_glove.6B.300d') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/average_word_embeddings_glove.6B.300d) ## Full Model Architecture ``` SentenceTransformer( (0): WordEmbeddings( (emb_layer): Embedding(400001, 300) ) (1): Pooling({'word_embedding_dimension': 300, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

{"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"}

DataikuNLP/average_word_embeddings_glove.6B.300d

2022-03-02T23:29:04+00:00

TAGS #sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us

# average_word_embeddings_glove.6B.300d This model is a copy of this model repository from sentence-transformers at the specific commit '5d2b7d1c127036ae98b9d487eca4d48744edc709'. This is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL ## Full Model Architecture ## Citing & Authors This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

fill-mask

transformers

# CamemBERT: a Tasty French Language Model **This model is a copy of [this model repository](https://huggingface.co/camembert-base) at the specific commit `482393b6198924f9da270b1aaf37d238aafca99b`.** ## Introduction [CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model. It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains. For further information or requests, please go to [Camembert Website](https://camembert-model.fr/) ## Pre-trained models | Model | #params | Arch. | Training data | |--------------------------------|--------------------------------|-------|-----------------------------------| | `camembert-base` | 110M | Base | OSCAR (138 GB of text) | | `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) | | `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) | | `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) | | `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) | | `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) | ## How to use CamemBERT with HuggingFace ##### Load CamemBERT and its sub-word tokenizer : ```python from transformers import CamembertModel, CamembertTokenizer # You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large". tokenizer = CamembertTokenizer.from_pretrained("camembert-base") camembert = CamembertModel.from_pretrained("camembert-base") camembert.eval() # disable dropout (or leave in train mode to finetune) ``` ##### Filling masks using pipeline ```python from transformers import pipeline camembert_fill_mask = pipeline("fill-mask", model="camembert-base", tokenizer="camembert-base") results = camembert_fill_mask("Le camembert est <mask> :)") # results #[{'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.4909103214740753, 'token': 7200}, # {'sequence': '<s> Le camembert est excellent :)</s>', 'score': 0.10556930303573608, 'token': 2183}, # {'sequence': '<s> Le camembert est succulent :)</s>', 'score': 0.03453315049409866, 'token': 26202}, # {'sequence': '<s> Le camembert est meilleur :)</s>', 'score': 0.03303130343556404, 'token': 528}, # {'sequence': '<s> Le camembert est parfait :)</s>', 'score': 0.030076518654823303, 'token': 1654}] ``` ##### Extract contextual embedding features from Camembert output ```python import torch # Tokenize in sub-words with SentencePiece tokenized_sentence = tokenizer.tokenize("J'aime le camembert !") # ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!'] # 1-hot encode and add special starting and end tokens encoded_sentence = tokenizer.encode(tokenized_sentence) # [5, 121, 11, 660, 16, 730, 25543, 110, 83, 6] # NB: Can be done in one step : tokenize.encode("J'aime le camembert !") # Feed tokens to Camembert as a torch tensor (batch dim 1) encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0) embeddings, _ = camembert(encoded_sentence) # embeddings.detach() # embeddings.size torch.Size([1, 10, 768]) # tensor([[[-0.0254, 0.0235, 0.1027, ..., -0.1459, -0.0205, -0.0116], # [ 0.0606, -0.1811, -0.0418, ..., -0.1815, 0.0880, -0.0766], # [-0.1561, -0.1127, 0.2687, ..., -0.0648, 0.0249, 0.0446], # ..., ``` ##### Extract contextual embedding features from all Camembert layers ```python from transformers import CamembertConfig # (Need to reload the model with new config) config = CamembertConfig.from_pretrained("camembert-base", output_hidden_states=True) camembert = CamembertModel.from_pretrained("camembert-base", config=config) embeddings, _, all_layer_embeddings = camembert(encoded_sentence) # all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers) all_layer_embeddings[5] # layer 5 contextual embedding : size torch.Size([1, 10, 768]) #tensor([[[-0.0032, 0.0075, 0.0040, ..., -0.0025, -0.0178, -0.0210], # [-0.0996, -0.1474, 0.1057, ..., -0.0278, 0.1690, -0.2982], # [ 0.0557, -0.0588, 0.0547, ..., -0.0726, -0.0867, 0.0699], # ..., ``` ## Authors CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot. ## Citation If you use our work, please cite: ```bibtex @inproceedings{martin2020camembert, title={CamemBERT: a Tasty French Language Model}, author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t}, booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, year={2020} } ```

{"language": "fr", "license": "mit", "datasets": ["oscar"]}

DataikuNLP/camembert-base

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #camembert #fill-mask #fr #dataset-oscar #arxiv-1911.03894 #license-mit #autotrain_compatible #endpoints_compatible #region-us

CamemBERT: a Tasty French Language Model ======================================== This model is a copy of this model repository at the specific commit '482393b6198924f9da270b1aaf37d238aafca99b'. Introduction ------------ CamemBERT is a state-of-the-art language model for French based on the RoBERTa model. It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains. For further information or requests, please go to Camembert Website Pre-trained models ------------------ How to use CamemBERT with HuggingFace ------------------------------------- ##### Load CamemBERT and its sub-word tokenizer : ##### Filling masks using pipeline ##### Extract contextual embedding features from Camembert output ##### Extract contextual embedding features from all Camembert layers Authors ------- CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot. If you use our work, please cite:

sentence-similarity

sentence-transformers

# DataikuNLP/distiluse-base-multilingual-cased-v1 **This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1) from sentence-transformers at the specific commit `3a706e4d65c04f868c4684adfd4da74141be8732`.** This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v1') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distiluse-base-multilingual-cased-v1) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (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}) (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

{"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}

DataikuNLP/distiluse-base-multilingual-cased-v1

2022-03-02T23:29:04+00:00

TAGS #sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us

# DataikuNLP/distiluse-base-multilingual-cased-v1 This model is a copy of this model repository from sentence-transformers at the specific commit '3a706e4d65c04f868c4684adfd4da74141be8732'. This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL ## Full Model Architecture ## Citing & Authors This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

sentence-similarity

sentence-transformers

# DataikuNLP/paraphrase-MiniLM-L6-v2 **This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2/) from sentence-transformers at the specific commit `c4dfcde8a3e3e17e85cd4f0ec1925a266187f48e`.** This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), 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. ```python 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 = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2') # 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, max 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](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-MiniLM-L6-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

{"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}

DataikuNLP/paraphrase-MiniLM-L6-v2

2022-03-02T23:29:04+00:00

TAGS #sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us

# DataikuNLP/paraphrase-MiniLM-L6-v2 This model is a copy of this model repository from sentence-transformers at the specific commit 'c4dfcde8a3e3e17e85cd4f0ec1925a266187f48e'. This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## 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. ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL ## Full Model Architecture ## Citing & Authors This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

sentence-similarity

sentence-transformers

# DataikuNLP/paraphrase-albert-small-v2 **This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/paraphrase-albert-small-v2/) from sentence-transformers at the specific commit `1eb1996223dd90a4c25be2fc52f6f336419a0d52`.** This is a [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. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/paraphrase-albert-small-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), 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. ```python 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 = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-albert-small-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-albert-small-v2') # 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, max 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](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-albert-small-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 100, 'do_lower_case': False}) with Transformer model: AlbertModel (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 This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

{"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}

DataikuNLP/paraphrase-albert-small-v2

2022-03-02T23:29:04+00:00

TAGS #sentence-transformers #pytorch #albert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us

# DataikuNLP/paraphrase-albert-small-v2 This model is a copy of this model repository from sentence-transformers at the specific commit '1eb1996223dd90a4c25be2fc52f6f336419a0d52'. 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. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## 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. ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL ## Full Model Architecture ## Citing & Authors This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

sentence-similarity

sentence-transformers

# DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 **This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) from sentence-transformers at the specific commit `d66eff4d8a8598f264f166af8db67f7797164651`.** This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), 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. ```python 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 = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2') # 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, max 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](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

{"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}

DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2

2022-03-02T23:29:04+00:00

TAGS #sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us

# DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 This model is a copy of this model repository from sentence-transformers at the specific commit 'd66eff4d8a8598f264f166af8db67f7797164651'. This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## 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. ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL ## Full Model Architecture ## Citing & Authors This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

fill-mask

transformers

Hugging Face's logo --- language: am datasets: --- # bert-base-multilingual-cased-finetuned-amharic ## Model description **bert-base-multilingual-cased-finetuned-amharic** is a **Amharic BERT** model obtained by replacing mBERT vocabulary by amharic vocabulary because the language was not supported, and fine-tuning **bert-base-multilingual-cased** model on Amharic language texts. It provides **better performance** than the multilingual Amharic on named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Amharic corpus using Amharic vocabulary. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-amharic') >>> unmasker("የአሜሪካ የአፍሪካ ቀንድ ልዩ መልዕክተኛ ጄፈሪ ፌልትማን በአራት አገራት የሚያደጉትን [MASK] መጀመራቸውን የአሜሪካ የውጪ ጉዳይ ሚንስቴር አስታወቀ።") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on [Amharic CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| mBERT F1 | am_bert F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 0.0 | 60.89 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/bert-base-multilingual-cased-finetuned-amharic

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us

Hugging Face's logo ------------------- language: am datasets: --- bert-base-multilingual-cased-finetuned-amharic ============================================== Model description ----------------- bert-base-multilingual-cased-finetuned-amharic is a Amharic BERT model obtained by replacing mBERT vocabulary by amharic vocabulary because the language was not supported, and fine-tuning bert-base-multilingual-cased model on Amharic language texts. It provides better performance than the multilingual Amharic on named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Amharic corpus using Amharic vocabulary. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on Amharic CC-100 Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, mBERT F1: 0.0, am\_bert F1: 60.89 ### BibTeX entry and citation info By David Adelani

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transformers

Hugging Face's logo --- language: ha datasets: --- # bert-base-multilingual-cased-finetuned-hausa ## Model description **bert-base-multilingual-cased-finetuned-hausa** is a **Hausa BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Hausa language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Hausa corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-hausa') >>> unmasker("Shugaban [MASK] Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci") [{'sequence': '[CLS] Shugaban Nigeria Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.9762618541717529, 'token': 22045, 'token_str': 'Nigeria'}, {'sequence': '[CLS] Shugaban Ka Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.007239189930260181, 'token': 25444, 'token_str': 'Ka'}, {'sequence': '[CLS] Shugaban, Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.001990817254409194, 'token': 117, 'token_str': ','}, {'sequence': '[CLS] Shugaban Ghana Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.001566368737258017, 'token': 28682, 'token_str': 'Ghana'}, {'sequence': '[CLS] Shugabanmu Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.0009375187801197171, 'token': 11717, 'token_str': '##mu'}] ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on [Hausa CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| mBERT F1 | ha_bert F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 86.65 | 91.31 [VOA Hausa Textclass](https://huggingface.co/datasets/hausa_voa_topics) | 84.76 | 90.98 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/bert-base-multilingual-cased-finetuned-hausa

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: ha datasets: --- bert-base-multilingual-cased-finetuned-hausa ============================================ Model description ----------------- bert-base-multilingual-cased-finetuned-hausa is a Hausa BERT model obtained by fine-tuning bert-base-multilingual-cased model on Hausa language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Hausa corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on Hausa CC-100 Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, mBERT F1: 86.65, ha\_bert F1: 91.31 Dataset: VOA Hausa Textclass, mBERT F1: 84.76, ha\_bert F1: 90.98 ### BibTeX entry and citation info By David Adelani

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transformers

Hugging Face's logo --- language: ig datasets: --- # bert-base-multilingual-cased-finetuned-igbo ## Model description **bert-base-multilingual-cased-finetuned-igbo** is a **Igbo BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Igbo language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Igbo corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-igbo') >>> unmasker("Reno Omokri na Gọọmentị [MASK] enweghị ihe ha ga-eji hiwe ya bụ mmachi.") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 + OPUS CC-Align + [IGBO NLP Corpus](https://github.com/IgnatiusEzeani/IGBONLP) +[Igbo CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| mBERT F1 | ig_bert F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 85.11 | 86.75 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/bert-base-multilingual-cased-finetuned-igbo

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: ig datasets: --- bert-base-multilingual-cased-finetuned-igbo =========================================== Model description ----------------- bert-base-multilingual-cased-finetuned-igbo is a Igbo BERT model obtained by fine-tuning bert-base-multilingual-cased model on Igbo language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Igbo corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on JW300 + OPUS CC-Align + IGBO NLP Corpus +Igbo CC-100 Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, mBERT F1: 85.11, ig\_bert F1: 86.75 ### BibTeX entry and citation info By David Adelani

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transformers

Hugging Face's logo --- language: rw datasets: --- # bert-base-multilingual-cased-finetuned-kinyarwanda ## Model description **bert-base-multilingual-cased-finetuned-kinyarwanda** is a **Kinyarwanda BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Kinyarwanda language texts. It provides **better performance** than the multilingual BERT on named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Kinyarwanda corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda') >>> unmasker("Twabonye ko igihe mu [MASK] hazaba hari ikirango abantu bakunze") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 + [KIRNEWS](https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus) + [BBC Gahuza](https://www.bbc.com/gahuza) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| mBERT F1 | rw_bert F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 72.20 | 77.57 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: rw datasets: --- bert-base-multilingual-cased-finetuned-kinyarwanda ================================================== Model description ----------------- bert-base-multilingual-cased-finetuned-kinyarwanda is a Kinyarwanda BERT model obtained by fine-tuning bert-base-multilingual-cased model on Kinyarwanda language texts. It provides better performance than the multilingual BERT on named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Kinyarwanda corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on JW300 + KIRNEWS + BBC Gahuza Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, mBERT F1: 72.20, rw\_bert F1: 77.57 ### BibTeX entry and citation info By David Adelani

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Hugging Face's logo --- language: lg datasets: --- # bert-base-multilingual-cased-finetuned-luganda ## Model description **bert-base-multilingual-cased-finetuned-luganda** is a **Luganda BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Luganda language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Luganda corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-luganda') >>> unmasker("Ffe tulwanyisa abo abaagala okutabangula [MASK], Kimuli bwe yategeezezza.") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 + [BUKKEDDE](https://github.com/masakhane-io/masakhane-ner/tree/main/text_by_language/luganda) +[Luganda CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| mBERT F1 | lg_bert F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 80.36 | 84.70 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/bert-base-multilingual-cased-finetuned-luganda

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: lg datasets: --- bert-base-multilingual-cased-finetuned-luganda ============================================== Model description ----------------- bert-base-multilingual-cased-finetuned-luganda is a Luganda BERT model obtained by fine-tuning bert-base-multilingual-cased model on Luganda language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Luganda corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on JW300 + BUKKEDDE +Luganda CC-100 Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, mBERT F1: 80.36, lg\_bert F1: 84.70 ### BibTeX entry and citation info By David Adelani

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transformers

Hugging Face's logo --- language: luo datasets: --- # bert-base-multilingual-cased-finetuned-luo ## Model description **bert-base-multilingual-cased-finetuned-luo** is a **Luo BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Luo language texts. It provides **better performance** than the multilingual BERT on named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Luo corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-luo') >>> unmasker("Obila ma Changamwe [MASK] pedho achije angwen mag njore") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| mBERT F1 | luo_bert F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 74.22 | 75.59 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/bert-base-multilingual-cased-finetuned-luo

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: luo datasets: --- bert-base-multilingual-cased-finetuned-luo ========================================== Model description ----------------- bert-base-multilingual-cased-finetuned-luo is a Luo BERT model obtained by fine-tuning bert-base-multilingual-cased model on Luo language texts. It provides better performance than the multilingual BERT on named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Luo corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on JW300 Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, mBERT F1: 74.22, luo\_bert F1: 75.59 ### BibTeX entry and citation info By David Adelani

fill-mask

transformers

Hugging Face's logo --- language: pcm datasets: --- # bert-base-multilingual-cased-finetuned-naija ## Model description **bert-base-multilingual-cased-finetuned-naija** is a **Nigerian-Pidgin BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Nigerian-Pidgin language texts. It provides **better performance** than the multilingual BERT on named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Nigerian-Pidgin corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-naija') >>> unmasker("Another attack on ambulance happen for Koforidua in March [MASK] year where robbers kill Ambulance driver") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 + [BBC Pidgin](https://www.bbc.com/pidgin) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| mBERT F1 | pcm_bert F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.23 | 89.95 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/bert-base-multilingual-cased-finetuned-naija

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: pcm datasets: --- bert-base-multilingual-cased-finetuned-naija ============================================ Model description ----------------- bert-base-multilingual-cased-finetuned-naija is a Nigerian-Pidgin BERT model obtained by fine-tuning bert-base-multilingual-cased model on Nigerian-Pidgin language texts. It provides better performance than the multilingual BERT on named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Nigerian-Pidgin corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on JW300 + BBC Pidgin Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, mBERT F1: 87.23, pcm\_bert F1: 89.95 ### BibTeX entry and citation info By David Adelani

fill-mask

transformers

Hugging Face's logo --- language: ha datasets: --- # bert-base-multilingual-cased-finetuned-swahili ## Model description **bert-base-multilingual-cased-finetuned-swahili** is a **Swahili BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Swahili language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Swahili corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-swahili') >>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko [MASK] kwamba "hakuna uhalifu ulitendwa") [{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa', 'score': 0.31642526388168335, 'token': 10728, 'token_str': 'Paris'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Rwanda kwamba hakuna uhalifu ulitendwa', 'score': 0.15753623843193054, 'token': 57557, 'token_str': 'Rwanda'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Burundi kwamba hakuna uhalifu ulitendwa', 'score': 0.07211585342884064, 'token': 57824, 'token_str': 'Burundi'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa', 'score': 0.029844321310520172, 'token': 10688, 'token_str': 'France'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Senegal kwamba hakuna uhalifu ulitendwa', 'score': 0.0265930388122797, 'token': 38052, 'token_str': 'Senegal'}] ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on [Swahili CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| mBERT F1 | sw_bert F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 86.80 | 89.36 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/bert-base-multilingual-cased-finetuned-swahili

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: ha datasets: --- bert-base-multilingual-cased-finetuned-swahili ============================================== Model description ----------------- bert-base-multilingual-cased-finetuned-swahili is a Swahili BERT model obtained by fine-tuning bert-base-multilingual-cased model on Swahili language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Swahili corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on Swahili CC-100 Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, mBERT F1: 86.80, sw\_bert F1: 89.36 ### BibTeX entry and citation info By David Adelani

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transformers

Hugging Face's logo --- language: wo datasets: --- # bert-base-multilingual-cased-finetuned-wolof ## Model description **bert-base-multilingual-cased-finetuned-wolof** is a **Wolof BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Wolof language texts. It provides **better performance** than the multilingual BERT on named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Wolof corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-wolof') >>> unmasker("Màkki Sàll feeñal na ay xalaatam ci mbir yu am solo yu soxal [MASK] ak Afrik.") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on [Bible OT](http://biblewolof.com/) + [OPUS](https://opus.nlpl.eu/) + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| mBERT F1 | wo_bert F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 64.52 | 69.43 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/bert-base-multilingual-cased-finetuned-wolof

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: wo datasets: --- bert-base-multilingual-cased-finetuned-wolof ============================================ Model description ----------------- bert-base-multilingual-cased-finetuned-wolof is a Wolof BERT model obtained by fine-tuning bert-base-multilingual-cased model on Wolof language texts. It provides better performance than the multilingual BERT on named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Wolof corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on Bible OT + OPUS + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online) Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, mBERT F1: 64.52, wo\_bert F1: 69.43 ### BibTeX entry and citation info By David Adelani

fill-mask

transformers

Hugging Face's logo --- language: yo datasets: --- # bert-base-multilingual-cased-finetuned-yoruba ## Model description **bert-base-multilingual-cased-finetuned-yoruba** is a **Yoruba BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Yorùbá language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Yorùbá corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-yoruba') >>> unmasker("Arẹmọ Phillip to jẹ ọkọ [MASK] Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun") [{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Mary Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.1738305538892746, 'token': 12176, 'token_str': 'Mary'}, {'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Queen Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.16382873058319092, 'token': 13704, 'token_str': 'Queen'}, {'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ ti Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.13272495567798615, 'token': 14382, 'token_str': 'ti'}, {'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ King Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.12823280692100525, 'token': 11515, 'token_str': 'King'}, {'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Lady Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.07841219753026962, 'token': 14005, 'token_str': 'Lady'}] ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on Bible, JW300, [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), [Yoruba Embedding corpus](https://huggingface.co/datasets/yoruba_text_c3) and [CC-Aligned](https://opus.nlpl.eu/), Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends. ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| mBERT F1 | yo_bert F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 78.97 | 82.58 [BBC Yorùbá Textclass](https://huggingface.co/datasets/yoruba_bbc_topics) | 75.13 | 79.11 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/bert-base-multilingual-cased-finetuned-yoruba

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: yo datasets: --- bert-base-multilingual-cased-finetuned-yoruba ============================================= Model description ----------------- bert-base-multilingual-cased-finetuned-yoruba is a Yoruba BERT model obtained by fine-tuning bert-base-multilingual-cased model on Yorùbá language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Yorùbá corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on Bible, JW300, Menyo-20k, Yoruba Embedding corpus and CC-Aligned, Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends. Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, mBERT F1: 78.97, yo\_bert F1: 82.58 Dataset: BBC Yorùbá Textclass, mBERT F1: 75.13, yo\_bert F1: 79.11 ### BibTeX entry and citation info By David Adelani

token-classification

transformers

Hugging Face's logo --- language: - ha - ig - rw - lg - luo - pcm - sw - wo - yo - multilingual datasets: - masakhaner --- # bert-base-multilingual-cased-masakhaner ## Model description **bert-base-multilingual-cased-masakhaner** is the first **Named Entity Recognition** model for 9 African languages (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned mBERT base model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Davlan/bert-base-multilingual-cased-masakhaner") model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-base-multilingual-cased-masakhaner") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria" ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-DATE |Beginning of a DATE entity right after another DATE entity I-DATE |DATE entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location ## Training procedure This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus. ## Eval results on Test set (F-score) language|F1-score -|- hau |88.66 ibo |85.72 kin |71.94 lug |81.73 luo |77.39 pcm |88.96 swa |88.23 wol |66.27 yor |80.09 ### BibTeX entry and citation info ``` @article{adelani21tacl, title = {Masakha{NER}: Named Entity Recognition for African Languages}, author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei}, journal = {Transactions of the Association for Computational Linguistics (TACL)}, month = {}, url = {https://arxiv.org/abs/2103.11811}, year = {2021} } ```

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Davlan/bert-base-multilingual-cased-masakhaner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #bert #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: * ha * ig * rw * lg * luo * pcm * sw * wo * yo * multilingual datasets: * masakhaner --- bert-base-multilingual-cased-masakhaner ======================================= Model description ----------------- bert-base-multilingual-cased-masakhaner is the first Named Entity Recognition model for 9 African languages (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned mBERT base model. It achieves the state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane MasakhaNER dataset. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for NER. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane MasakhaNER dataset The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus. Eval results on Test set (F-score) ---------------------------------- ### BibTeX entry and citation info

token-classification

transformers

Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- # bert-base-multilingual-cased-ner-hrl ## Model description **bert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned mBERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl") model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute." ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data The training data for the 10 languages are from: Language|Dataset -|- Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/) German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/) English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/) Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/) French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio) Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html) Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities) Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/) Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese) Chinese | [MSRA](https://huggingface.co/datasets/msra_ner) The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location ## Training procedure This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.

{"license": "afl-3.0"}

Davlan/bert-base-multilingual-cased-ner-hrl

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #onnx #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us

Hugging Face's logo ------------------- language: * ar * de * en * es * fr * it * lv * nl * pt * zh * multilingual --- bert-base-multilingual-cased-ner-hrl ==================================== Model description ----------------- bert-base-multilingual-cased-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned mBERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for NER. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- The training data for the 10 languages are from: The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Training procedure ------------------ This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.

text2text-generation

transformers

Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # byt5-base-eng-yor-mt ## Model description **byt5-base-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned byt5-base model. It establishes a **strong baseline** for automatically translating texts from English to Yorùbá. Specifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning byt5-base achieves **12.23 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/byt5-base-eng-yor-mt

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #t5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us

Hugging Face's logo --- language: - yo - en datasets: - JW300 + Menyo-20k --- # byt5-base-eng-yor-mt ## Model description byt5-base-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. Specifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and Menyo-20k dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning byt5-base achieves 12.23 BLEU on Menyo-20k test set while mt5-base achieves 9.82 ### BibTeX entry and citation info By David Adelani

text2text-generation

transformers

Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # byt5-base-yor-eng-mt ## Model description **byt5-base-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned byt5-base model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English. Specifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning byt5-base achieves 14.05 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/byt5-base-yor-eng-mt

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #t5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

Hugging Face's logo --- language: - yo - en datasets: - JW300 + Menyo-20k --- # byt5-base-yor-eng-mt ## Model description byt5-base-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. Specifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and Menyo-20k dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning byt5-base achieves 14.05 BLEU on Menyo-20k test set while mt5-base achieves 15.57 ### BibTeX entry and citation info By David Adelani

token-classification

transformers

Hugging Face's logo --- language: - ha - ig - rw - lg - luo - pcm - sw - wo - yo - multilingual datasets: - masakhaner --- # bert-base-multilingual-cased-masakhaner ## Model description **distilbert-base-multilingual-cased-masakhaner** is the first **Named Entity Recognition** model for 9 African languages (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned BERT base model. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Davlan/distilbert-base-multilingual-cased-masakhaner") model = AutoModelForTokenClassification.from_pretrained("Davlan/distilbert-base-multilingual-cased-masakhaner") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria" ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-DATE |Beginning of a DATE entity right after another DATE entity I-DATE |DATE entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location ## Training procedure This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus. ## Eval results on Test set (F-score) language|F1-score -|- hau |88.88 ibo |84.87 kin |74.19 lug |78.43 luo |73.32 pcm |87.98 swa |86.20 wol |64.67 yor |78.10 ### BibTeX entry and citation info ``` @article{adelani21tacl, title = {Masakha{NER}: Named Entity Recognition for African Languages}, author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei}, journal = {Transactions of the Association for Computational Linguistics (TACL)}, month = {}, url = {https://arxiv.org/abs/2103.11811}, year = {2021} } ```

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Davlan/distilbert-base-multilingual-cased-masakhaner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #distilbert #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: * ha * ig * rw * lg * luo * pcm * sw * wo * yo * multilingual datasets: * masakhaner --- bert-base-multilingual-cased-masakhaner ======================================= Model description ----------------- distilbert-base-multilingual-cased-masakhaner is the first Named Entity Recognition model for 9 African languages (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned BERT base model. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane MasakhaNER dataset. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for NER. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on 9 African NER datasets (Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane MasakhaNER dataset The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus. Eval results on Test set (F-score) ---------------------------------- ### BibTeX entry and citation info

token-classification

transformers

Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- # distilbert-base-multilingual-cased-ner-hrl ## Model description **distilbert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned Distiled BERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl") model = AutoModelForTokenClassification.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute." ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data The training data for the 10 languages are from: Language|Dataset -|- Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/) German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/) English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/) Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/) French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio) Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html) Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities) Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/) Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese) Chinese | [MSRA](https://huggingface.co/datasets/msra_ner) The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location ## Training procedure This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.

{"license": "afl-3.0"}

Davlan/distilbert-base-multilingual-cased-ner-hrl

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #safetensors #distilbert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us

Hugging Face's logo ------------------- language: * ar * de * en * es * fr * it * lv * nl * pt * zh * multilingual --- distilbert-base-multilingual-cased-ner-hrl ========================================== Model description ----------------- distilbert-base-multilingual-cased-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned Distiled BERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for NER. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- The training data for the 10 languages are from: The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Training procedure ------------------ This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.

text2text-generation

transformers

Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # m2m100_418M-eng-yor-mt ## Model description **m2m100_418M-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned facebook/m2m100_418M model. It establishes a **strong baseline** for automatically translating texts from English to Yorùbá. Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning m2m100_418M achieves **13.39 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/m2m100_418M-eng-yor-mt

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #m2m_100 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo --- language: - yo - en datasets: - JW300 + Menyo-20k --- # m2m100_418M-eng-yor-mt ## Model description m2m100_418M-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k. #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 corpus and Menyo-20k dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning m2m100_418M achieves 13.39 BLEU on Menyo-20k test set while mt5-base achieves 9.82 ### BibTeX entry and citation info By David Adelani

text2text-generation

transformers

Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # m2m100_418M-eng-yor-mt ## Model description **m2m100_418M-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English. Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning m2m100_418M achieves **16.76 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/m2m100_418M-yor-eng-mt

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #m2m_100 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #region-us

Hugging Face's logo --- language: - yo - en datasets: - JW300 + Menyo-20k --- # m2m100_418M-eng-yor-mt ## Model description m2m100_418M-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k. #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 corpus and Menyo-20k dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning m2m100_418M achieves 16.76 BLEU on Menyo-20k test set while mt5-base achieves 15.57 ### BibTeX entry and citation info By David Adelani

text2text-generation

transformers

Hugging Face's logo --- language: yo datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # mT5_base_yoruba_adr ## Model description **mT5_base_yoruba_adr** is a **automatic diacritics restoration** model for Yorùbá language based on a fine-tuned mT5-base model. It achieves the **state-of-the-art performance** for adding the correct diacritics or tonal marks to Yorùbá texts. Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for ADR. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("") model = AutoModelForTokenClassification.from_pretrained("") nlp = pipeline("", model=model, tokenizer=tokenizer) example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria" ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (BLEU score) 64.63 BLEU on [Global Voices test set](https://arxiv.org/abs/2003.10564) 70.27 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) ### BibTeX entry and citation info By Jesujoba Alabi and David Adelani ``` ```

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Davlan/mT5_base_yoruba_adr

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #mt5 #text2text-generation #arxiv-2003.10564 #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

Hugging Face's logo --- language: yo datasets: - JW300 + Menyo-20k --- # mT5_base_yoruba_adr ## Model description mT5_base_yoruba_adr is a automatic diacritics restoration model for Yorùbá language based on a fine-tuned mT5-base model. It achieves the state-of-the-art performance for adding the correct diacritics or tonal marks to Yorùbá texts. Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for ADR. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 Yorùbá corpus and Menyo-20k dataset ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (BLEU score) 64.63 BLEU on Global Voices test set 70.27 BLEU on Menyo-20k test set ### BibTeX entry and citation info By Jesujoba Alabi and David Adelani

text2text-generation

transformers

Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # mbart50-large-eng-yor-mt ## Model description **mbart50-large-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned facebook/mbart-large-50 model. It establishes a **strong baseline** for automatically translating texts from English to Yorùbá. Specifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model. #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning mbarr50-large achieves **13.39 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/mbart50-large-eng-yor-mt

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #region-us

Hugging Face's logo --- language: - yo - en datasets: - JW300 + Menyo-20k --- # mbart50-large-eng-yor-mt ## Model description mbart50-large-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. Specifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model. #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and Menyo-20k dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning mbarr50-large achieves 13.39 BLEU on Menyo-20k test set while mt5-base achieves 9.82 ### BibTeX entry and citation info By David Adelani

text2text-generation

transformers

Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # mbart50-large-yor-eng-mt ## Model description **mbart50-large-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned facebook/mbart-large-50 model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English. Specifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model. #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning mbart50-large achieves **15.88 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/mbart50-large-yor-eng-mt

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo --- language: - yo - en datasets: - JW300 + Menyo-20k --- # mbart50-large-yor-eng-mt ## Model description mbart50-large-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. Specifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model. #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and Menyo-20k dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning mbart50-large achieves 15.88 BLEU on Menyo-20k test set while mt5-base achieves 15.57 ### BibTeX entry and citation info By David Adelani

text2text-generation

transformers

Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # mT5_base_eng_yor_mt ## Model description **mT5_base_yor_eng_mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned mT5-base model. It establishes a **strong baseline** for automatically translating texts from English to Yorùbá. Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for MT. ```python from transformers import MT5ForConditionalGeneration, T5Tokenizer model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_eng_yor_mt") tokenizer = T5Tokenizer.from_pretrained("google/mt5-base") input_string = "Where are you?" inputs = tokenizer.encode(input_string, return_tensors="pt") generated_tokens = model.generate(inputs) results = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) print(results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (BLEU score) 9.82 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/mt5_base_eng_yor_mt

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

Hugging Face's logo --- language: - yo - en datasets: - JW300 + Menyo-20k --- # mT5_base_eng_yor_mt ## Model description mT5_base_yor_eng_mt is a machine translation model from English language to Yorùbá language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for MT. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and Menyo-20k dataset ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (BLEU score) 9.82 BLEU on Menyo-20k test set ### BibTeX entry and citation info By David Adelani

text2text-generation

transformers

Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # mT5_base_yor_eng_mt ## Model description **mT5_base_yor_eng_mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English. Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for MT. ```python from transformers import MT5ForConditionalGeneration, T5Tokenizer model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_yor_eng_mt") tokenizer = T5Tokenizer.from_pretrained("google/mt5-base") input_string = "Akọni ajìjàgbara obìnrin tó sun àtìmalé torí owó orí" inputs = tokenizer.encode(input_string, return_tensors="pt") generated_tokens = model.generate(inputs) results = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) print(results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (BLEU score) 15.57 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/mt5_base_yor_eng_mt

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

Hugging Face's logo --- language: - yo - en datasets: - JW300 + Menyo-20k --- # mT5_base_yor_eng_mt ## Model description mT5_base_yor_eng_mt is a machine translation model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for MT. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 Yorùbá corpus and Menyo-20k dataset ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (BLEU score) 15.57 BLEU on Menyo-20k test set ### BibTeX entry and citation info By David Adelani

text-classification

transformers

Hugging Face's logo --- language: - hau - ibo - pcm - yor - multilingual --- # naija-twitter-sentiment-afriberta-large ## Model description **naija-twitter-sentiment-afriberta-large** is the first multilingual twitter **sentiment classification** model for four (4) Nigerian languages (Hausa, Igbo, Nigerian Pidgin, and Yorùbá) based on a fine-tuned castorini/afriberta_large large model. It achieves the **state-of-the-art performance** for the twitter sentiment classification task trained on the [NaijaSenti corpus](https://github.com/hausanlp/NaijaSenti). The model has been trained to classify tweets into 3 sentiment classes: negative, neutral and positive Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 4 Nigerian language datasets obtained from [NaijaSenti](https://github.com/hausanlp/NaijaSenti) dataset. ## Intended uses & limitations #### How to use You can use this model with Transformers for Sentiment Classification. ```python from transformers import AutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np from scipy.special import softmax MODEL = "Davlan/naija-twitter-sentiment-afriberta-large" tokenizer = AutoTokenizer.from_pretrained(MODEL) # PT model = AutoModelForSequenceClassification.from_pretrained(MODEL) text = "I like you" encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) id2label = {0:"positive", 1:"neutral", 2:"negative"} ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = id2label[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` #### Limitations and bias This model is limited by its training dataset and domain i.e Twitter. This may not generalize well for all use cases in different domains. ## Training procedure This model was trained on a single Nvidia RTX 2080 GPU with recommended hyperparameters from the [original NaijaSenti paper](https://arxiv.org/abs/2201.08277). ## Eval results on Test set (F-score), average over 5 runs. language|F1-score -|- hau |81.2 ibo |80.8 pcm |74.5 yor |80.4 ### BibTeX entry and citation info ``` @inproceedings{Muhammad2022NaijaSentiAN, title={NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis}, author={Shamsuddeen Hassan Muhammad and David Ifeoluwa Adelani and Sebastian Ruder and Ibrahim Said Ahmad and Idris Abdulmumin and Bello Shehu Bello and Monojit Choudhury and Chris C. Emezue and Saheed Salahudeen Abdullahi and Anuoluwapo Aremu and Alipio Jeorge and Pavel B. Brazdil}, year={2022} } ```

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Davlan/naija-twitter-sentiment-afriberta-large

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #xlm-roberta #text-classification #arxiv-2201.08277 #autotrain_compatible #endpoints_compatible #has_space #region-us

Hugging Face's logo ------------------- language: * hau * ibo * pcm * yor * multilingual --- naija-twitter-sentiment-afriberta-large ======================================= Model description ----------------- naija-twitter-sentiment-afriberta-large is the first multilingual twitter sentiment classification model for four (4) Nigerian languages (Hausa, Igbo, Nigerian Pidgin, and Yorùbá) based on a fine-tuned castorini/afriberta\_large large model. It achieves the state-of-the-art performance for the twitter sentiment classification task trained on the NaijaSenti corpus. The model has been trained to classify tweets into 3 sentiment classes: negative, neutral and positive Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 4 Nigerian language datasets obtained from NaijaSenti dataset. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers for Sentiment Classification. #### Limitations and bias This model is limited by its training dataset and domain i.e Twitter. This may not generalize well for all use cases in different domains. Training procedure ------------------ This model was trained on a single Nvidia RTX 2080 GPU with recommended hyperparameters from the original NaijaSenti paper. Eval results on Test set (F-score), average over 5 runs. -------------------------------------------------------- ### BibTeX entry and citation info

fill-mask

transformers

Hugging Face's logo --- language: am datasets: --- # xlm-roberta-base-finetuned-amharic ## Model description **xlm-roberta-base-finetuned-amharic** is a **Amharic RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Amharic language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Amharic corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-hausa') >>> unmasker("የአሜሪካ የአፍሪካ ቀንድ ልዩ መልዕክተኛ ጄፈሪ ፌልትማን በአራት አገራት የሚያደጉትን <mask> መጀመራቸውን የአሜሪካ የውጪ ጉዳይ ሚንስቴር አስታወቀ።") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on [Amharic CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | am_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 70.96 | 77.97 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/xlm-roberta-base-finetuned-amharic

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: am datasets: --- xlm-roberta-base-finetuned-amharic ================================== Model description ----------------- xlm-roberta-base-finetuned-amharic is a Amharic RoBERTa model obtained by fine-tuning xlm-roberta-base model on Amharic language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Amharic corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on Amharic CC-100 Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, XLM-R F1: 70.96, am\_roberta F1: 77.97 ### BibTeX entry and citation info By David Adelani

fill-mask

transformers

Hugging Face's logo --- language: ha datasets: --- # xlm-roberta-base-finetuned-hausa ## Model description **xlm-roberta-base-finetuned-hausa** is a **Hausa RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Hausa language texts. It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Hausa corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-hausa') >>> unmasker("Shugaban <mask> Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci") [{'sequence': '<s> Shugaban kasa Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.8104371428489685, 'token': 29762, 'token_str': '▁kasa'}, {'sequence': '<s> Shugaban Najeriya Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.17371904850006104, 'token': 49173, 'token_str': '▁Najeriya'}, {'sequence': '<s> Shugaban kasar Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.006917025428265333, 'token': 21221, 'token_str': '▁kasar'}, {'sequence': '<s> Shugaban Nigeria Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.005785710643976927, 'token': 72620, 'token_str': '▁Nigeria'}, {'sequence': '<s> Shugaban Kasar Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci</s>', 'score': 0.0010596115607768297, 'token': 170255, 'token_str': '▁Kasar'}] ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on [Hausa CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | ha_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 86.10 | 91.47 [VOA Hausa Textclass](https://huggingface.co/datasets/hausa_voa_topics) | | ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/xlm-roberta-base-finetuned-hausa

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: ha datasets: --- xlm-roberta-base-finetuned-hausa ================================ Model description ----------------- xlm-roberta-base-finetuned-hausa is a Hausa RoBERTa model obtained by fine-tuning xlm-roberta-base model on Hausa language texts. It provides better performance than the XLM-RoBERTa on text classification and named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Hausa corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on Hausa CC-100 Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, XLM-R F1: 86.10, ha\_roberta F1: 91.47 Dataset: VOA Hausa Textclass, XLM-R F1: , ha\_roberta F1: ### BibTeX entry and citation info By David Adelani

fill-mask

transformers

Hugging Face's logo --- language: ig datasets: --- # xlm-roberta-base-finetuned-igbo ## Model description **xlm-roberta-base-finetuned-igbo** is a **Igbo RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Hausa language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Igbo corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-igbo') >>> unmasker("Reno Omokri na Gọọmentị <mask> enweghị ihe ha ga-eji hiwe ya bụ mmachi.") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 + OPUS CC-Align + [IGBO NLP Corpus](https://github.com/IgnatiusEzeani/IGBONLP) +[Igbo CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | ig_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 84.51 | 87.74 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/xlm-roberta-base-finetuned-igbo

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: ig datasets: --- xlm-roberta-base-finetuned-igbo =============================== Model description ----------------- xlm-roberta-base-finetuned-igbo is a Igbo RoBERTa model obtained by fine-tuning xlm-roberta-base model on Hausa language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Igbo corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on JW300 + OPUS CC-Align + IGBO NLP Corpus +Igbo CC-100 Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, XLM-R F1: 84.51, ig\_roberta F1: 87.74 ### BibTeX entry and citation info By David Adelani

fill-mask

transformers

Hugging Face's logo --- language: rw datasets: --- # xlm-roberta-base-finetuned-kinyarwanda ## Model description **xlm-roberta-base-finetuned-kinyarwanda** is a **Kinyarwanda RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Kinyarwanda language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Kinyarwanda corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-kinyarwanda') >>> unmasker("Twabonye ko igihe mu <mask> hazaba hari ikirango abantu bakunze") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 + [KIRNEWS](https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus) + [BBC Gahuza](https://www.bbc.com/gahuza) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | rw_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 73.22 | 77.76 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/xlm-roberta-base-finetuned-kinyarwanda

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: rw datasets: --- xlm-roberta-base-finetuned-kinyarwanda ====================================== Model description ----------------- xlm-roberta-base-finetuned-kinyarwanda is a Kinyarwanda RoBERTa model obtained by fine-tuning xlm-roberta-base model on Kinyarwanda language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Kinyarwanda corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on JW300 + KIRNEWS + BBC Gahuza Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, XLM-R F1: 73.22, rw\_roberta F1: 77.76 ### BibTeX entry and citation info By David Adelani

fill-mask

transformers

Hugging Face's logo --- language: lg datasets: --- # xlm-roberta-base-finetuned-luganda ## Model description **xlm-roberta-base-finetuned-luganda** is a **Luganda RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Luganda language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Luganda corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-luganda') >>> unmasker("Ffe tulwanyisa abo abaagala okutabangula <mask>, Kimuli bwe yategeezezza.") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 + [BUKKEDDE](https://github.com/masakhane-io/masakhane-ner/tree/main/text_by_language/luganda) +[Luganda CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | lg_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 79.69 | 84.70 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/xlm-roberta-base-finetuned-luganda

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: lg datasets: --- xlm-roberta-base-finetuned-luganda ================================== Model description ----------------- xlm-roberta-base-finetuned-luganda is a Luganda RoBERTa model obtained by fine-tuning xlm-roberta-base model on Luganda language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Luganda corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on JW300 + BUKKEDDE +Luganda CC-100 Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, XLM-R F1: 79.69, lg\_roberta F1: 84.70 ### BibTeX entry and citation info By David Adelani

fill-mask

transformers

Hugging Face's logo --- language: luo datasets: --- # xlm-roberta-base-finetuned-luo ## Model description **xlm-roberta-base-finetuned-luo** is a **Luo RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Luo language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Luo corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-luo') >>> unmasker("Obila ma Changamwe <mask> pedho achije angwen mag njore") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | luo_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 74.86 | 75.27 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/xlm-roberta-base-finetuned-luo

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: luo datasets: --- xlm-roberta-base-finetuned-luo ============================== Model description ----------------- xlm-roberta-base-finetuned-luo is a Luo RoBERTa model obtained by fine-tuning xlm-roberta-base model on Luo language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Luo corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on JW300 Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, XLM-R F1: 74.86, luo\_roberta F1: 75.27 ### BibTeX entry and citation info By David Adelani

fill-mask

transformers

Hugging Face's logo --- language: pcm datasets: --- # xlm-roberta-base-finetuned-naija ## Model description **xlm-roberta-base-finetuned-naija** is a **Nigerian Pidgin RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Nigerian Pidgin language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Nigerian Pidgin corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-naija') >>> unmasker("Another attack on ambulance happen for Koforidua in March <mask> year where robbers kill Ambulance driver") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 + [BBC Pidgin](https://www.bbc.com/pidgin) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | pcm_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.26 | 90.00 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/xlm-roberta-base-finetuned-naija

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: pcm datasets: --- xlm-roberta-base-finetuned-naija ================================ Model description ----------------- xlm-roberta-base-finetuned-naija is a Nigerian Pidgin RoBERTa model obtained by fine-tuning xlm-roberta-base model on Nigerian Pidgin language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Nigerian Pidgin corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on JW300 + BBC Pidgin Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, XLM-R F1: 87.26, pcm\_roberta F1: 90.00 ### BibTeX entry and citation info By David Adelani

fill-mask

transformers

Hugging Face's logo --- language: sw datasets: --- # xlm-roberta-base-finetuned-swahili ## Model description **xlm-roberta-base-finetuned-swahili** is a **Swahili RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Swahili language texts. It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Swahili corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-swahili') >>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko <mask> kwamba hakuna uhalifu ulitendwa") [{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Ufaransa kwamba hakuna uhalifu ulitendwa', 'score': 0.5077782273292542, 'token': 190096, 'token_str': 'Ufaransa'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa', 'score': 0.3657738268375397, 'token': 7270, 'token_str': 'Paris'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Gabon kwamba hakuna uhalifu ulitendwa', 'score': 0.01592041552066803, 'token': 176392, 'token_str': 'Gabon'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa', 'score': 0.010881908237934113, 'token': 9942, 'token_str': 'France'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Marseille kwamba hakuna uhalifu ulitendwa', 'score': 0.009554869495332241, 'token': 185918, 'token_str': 'Marseille'}] ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on [Swahili CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | sw_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.55 | 89.46 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/xlm-roberta-base-finetuned-swahili

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: sw datasets: --- xlm-roberta-base-finetuned-swahili ================================== Model description ----------------- xlm-roberta-base-finetuned-swahili is a Swahili RoBERTa model obtained by fine-tuning xlm-roberta-base model on Swahili language texts. It provides better performance than the XLM-RoBERTa on text classification and named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Swahili corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on Swahili CC-100 Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, XLM-R F1: 87.55, sw\_roberta F1: 89.46 ### BibTeX entry and citation info By David Adelani

fill-mask

transformers

Hugging Face's logo --- language: wo datasets: --- # xlm-roberta-base-finetuned-wolof ## Model description **xlm-roberta-base-finetuned-luganda** is a **Wolof RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Wolof language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Wolof corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-wolof') >>> unmasker("Màkki Sàll feeñal na ay xalaatam ci mbir yu am solo yu soxal <mask> ak Afrik.") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on [Bible OT](http://biblewolof.com/) + [OPUS](https://opus.nlpl.eu/) + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | wo_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 63.86 | 68.31 ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/xlm-roberta-base-finetuned-wolof

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: wo datasets: --- xlm-roberta-base-finetuned-wolof ================================ Model description ----------------- xlm-roberta-base-finetuned-luganda is a Wolof RoBERTa model obtained by fine-tuning xlm-roberta-base model on Wolof language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Wolof corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on Bible OT + OPUS + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online) Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, XLM-R F1: 63.86, wo\_roberta F1: 68.31 ### BibTeX entry and citation info By David Adelani

fill-mask

transformers

Hugging Face's logo --- language: yo datasets: --- # xlm-roberta-base-finetuned-yoruba ## Model description **xlm-roberta-base-finetuned-yoruba** is a **Yoruba RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Yorùbá language texts. It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Yorùbá corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-yoruba') >>> unmasker("Arẹmọ Phillip to jẹ ọkọ <mask> Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun") [{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ Queen Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.24844281375408173, 'token': 44109, 'token_str': '▁Queen'}, {'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ ile Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.1665010154247284, 'token': 1350, 'token_str': '▁ile'}, {'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ ti Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.07604238390922546, 'token': 1053, 'token_str': '▁ti'}, {'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ baba Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.06353845447301865, 'token': 12878, 'token_str': '▁baba'}, {'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ Oba Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.03836742788553238, 'token': 82879, 'token_str': '▁Oba'}] ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on Bible, JW300, [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), [Yoruba Embedding corpus](https://huggingface.co/datasets/yoruba_text_c3) and [CC-Aligned](https://opus.nlpl.eu/), Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends. ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | yo_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 77.58 | 83.66 [BBC Yorùbá Textclass](https://huggingface.co/datasets/yoruba_bbc_topics) | | ### BibTeX entry and citation info By David Adelani ``` ```

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Davlan/xlm-roberta-base-finetuned-yoruba

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: yo datasets: --- xlm-roberta-base-finetuned-yoruba ================================= Model description ----------------- xlm-roberta-base-finetuned-yoruba is a Yoruba RoBERTa model obtained by fine-tuning xlm-roberta-base model on Yorùbá language texts. It provides better performance than the XLM-RoBERTa on text classification and named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Yorùbá corpus. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for masked token prediction. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on Bible, JW300, Menyo-20k, Yoruba Embedding corpus and CC-Aligned, Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends. Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU Eval results on Test set (F-score, average over 5 runs) ------------------------------------------------------- Dataset: MasakhaNER, XLM-R F1: 77.58, yo\_roberta F1: 83.66 Dataset: BBC Yorùbá Textclass, XLM-R F1: , yo\_roberta F1: ### BibTeX entry and citation info By David Adelani

token-classification

transformers

Hugging Face's logo --- language: - am - ha - ig - rw - lg - luo - pcm - sw - wo - yo - multilingual datasets: - masakhaner --- # xlm-roberta-base-masakhaner ## Model description **xlm-roberta-base-masakhaner** is the first **Named Entity Recognition** model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) based on a fine-tuned XLM-RoBERTa large model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-masakhaner") model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-base-masakhaner") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria" ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-DATE |Beginning of a DATE entity right after another DATE entity I-DATE |DATE entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location ## Training procedure This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus. ### BibTeX entry and citation info ``` @article{adelani21tacl, title = {Masakha{NER}: Named Entity Recognition for African Languages}, author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei}, journal = {Transactions of the Association for Computational Linguistics (TACL)}, month = {}, url = {https://arxiv.org/abs/2103.11811}, year = {2021} } ```

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Davlan/xlm-roberta-base-masakhaner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: * am * ha * ig * rw * lg * luo * pcm * sw * wo * yo * multilingual datasets: * masakhaner --- xlm-roberta-base-masakhaner =========================== Model description ----------------- xlm-roberta-base-masakhaner is the first Named Entity Recognition model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) based on a fine-tuned XLM-RoBERTa large model. It achieves the state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane MasakhaNER dataset. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for NER. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane MasakhaNER dataset The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus. ### BibTeX entry and citation info

token-classification

transformers

Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- # xlm-roberta-base-ner-hrl ## Model description **xlm-roberta-base-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on an aggregation of 10 high-resourced languages ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-ner-hrl") model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-base-ner-hrl") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute." ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data The training data for the 10 languages are from: Language|Dataset -|- Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/) German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/) English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/) Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/) French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio) Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html) Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities) Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/) Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese) Chinese | [MSRA](https://huggingface.co/datasets/msra_ner) The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location ## Training procedure This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.

{"license": "afl-3.0"}

Davlan/xlm-roberta-base-ner-hrl

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: * ar * de * en * es * fr * it * lv * nl * pt * zh * multilingual --- xlm-roberta-base-ner-hrl ======================== Model description ----------------- xlm-roberta-base-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on an aggregation of 10 high-resourced languages Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for NER. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- The training data for the 10 languages are from: The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Training procedure ------------------ This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.

token-classification

transformers

Hugging Face's logo --- language: - af - nr - nso - ss - st - tn - ts - ve - xh - zu - multilingual datasets: - masakhaner --- # xlm-roberta-base-sadilar-ner ## Model description **xlm-roberta-base-sadilar-ner** is the first **Named Entity Recognition** model for 10 South African languages (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) based on a fine-tuned XLM-RoBERTa large model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of South African languages datasets obtained from [SADILAR](https://www.sadilar.org/index.php/en/) dataset. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-sadilar-ner") model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-base-sadilar-ner") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Kuchaza kona ukuthi uMengameli uMnuz Cyril Ramaphosa, usebatshelile ukuthi uzosikhipha maduze isitifiketi." ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on 10 African NER datasets (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) [SADILAR](https://www.sadilar.org/index.php/en/) dataset The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location ### BibTeX entry and citation info ```

{}

Davlan/xlm-roberta-base-sadilar-ner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: * af * nr * nso * ss * st * tn * ts * ve * xh * zu * multilingual datasets: * masakhaner --- xlm-roberta-base-sadilar-ner ============================ Model description ----------------- xlm-roberta-base-sadilar-ner is the first Named Entity Recognition model for 10 South African languages (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) based on a fine-tuned XLM-RoBERTa large model. It achieves the state-of-the-art performance for the NER task. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of South African languages datasets obtained from SADILAR dataset. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for NER. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on 10 African NER datasets (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) SADILAR dataset The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: ### BibTeX entry and citation info '''

token-classification

transformers

Hugging Face's logo --- language: - ar - as - bn - ca - en - es - eu - fr - gu - hi - id - ig - mr - pa - pt - sw - ur - vi - yo - zh - multilingual datasets: - wikiann --- # xlm-roberta-base-wikiann-ner ## Model description **xlm-roberta-base-wikiann-ner** is the first **Named Entity Recognition** model for 20 languages (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese) based on a fine-tuned XLM-RoBERTa large model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of languages datasets obtained from [WikiANN](https://huggingface.co/datasets/wikiann) dataset. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base-wikiann-ner") model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-base-wikiann-ner") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Ìbọn ń ró kù kù gẹ́gẹ́ bí ọwọ́ ọ̀pọ̀ aráàlù ṣe tẹ ìbọn ní Kyiv láti dojú kọ Russia" ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on 20 NER datasets (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese)[wikiann](https://huggingface.co/datasets/wikiann). The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location ### BibTeX entry and citation info ```

{}

Davlan/xlm-roberta-base-wikiann-ner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: * ar * as * bn * ca * en * es * eu * fr * gu * hi * id * ig * mr * pa * pt * sw * ur * vi * yo * zh * multilingual datasets: * wikiann --- xlm-roberta-base-wikiann-ner ============================ Model description ----------------- xlm-roberta-base-wikiann-ner is the first Named Entity Recognition model for 20 languages (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese) based on a fine-tuned XLM-RoBERTa large model. It achieves the state-of-the-art performance for the NER task. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of languages datasets obtained from WikiANN dataset. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for NER. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on 20 NER datasets (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese)wikiann. The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: ### BibTeX entry and citation info '''

token-classification

transformers

Hugging Face's logo --- language: - amh - hau - ibo - kin - lug - luo - pcm - swa - wol - yor - multilingual datasets: - masakhaner --- # xlm-roberta-large-masakhaner ## Model description **xlm-roberta-large-masakhaner** is the first **Named Entity Recognition** model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) based on a fine-tuned XLM-RoBERTa large model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-large-masakhaner") model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-large-masakhaner") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria" ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-DATE |Beginning of a DATE entity right after another DATE entity I-DATE |DATE entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location ## Training procedure This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus. ## Eval results on Test set (F-score) language|F1-score -|- amh |75.76 hau |91.75 ibo |86.26 kin |76.38 lug |84.64 luo |80.65 pcm |89.55 swa |89.48 wol |70.70 yor |82.05 ### BibTeX entry and citation info ``` @article{adelani21tacl, title = {Masakha{NER}: Named Entity Recognition for African Languages}, author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei}, journal = {Transactions of the Association for Computational Linguistics (TACL)}, month = {}, url = {https://arxiv.org/abs/2103.11811}, year = {2021} } ```

{}

Davlan/xlm-roberta-large-masakhaner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: * amh * hau * ibo * kin * lug * luo * pcm * swa * wol * yor * multilingual datasets: * masakhaner --- xlm-roberta-large-masakhaner ============================ Model description ----------------- xlm-roberta-large-masakhaner is the first Named Entity Recognition model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) based on a fine-tuned XLM-RoBERTa large model. It achieves the state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane MasakhaNER dataset. Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for NER. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane MasakhaNER dataset The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Training procedure ------------------ This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the original MasakhaNER paper which trained & evaluated the model on MasakhaNER corpus. Eval results on Test set (F-score) ---------------------------------- ### BibTeX entry and citation info

token-classification

transformers

Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- # xlm-roberta-large-ner-hrl ## Model description **xlm-roberta-large-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa large model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 10 high-resourced languages ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-large-ner-hrl") model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-large-ner-hrl") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute." ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data The training data for the 10 languages are from: Language|Dataset -|- Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/) German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/) English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/) Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/) French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio) Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html) Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities) Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/) Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese) Chinese | [MSRA](https://huggingface.co/datasets/msra_ner) The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location ## Training procedure This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.

{"license": "afl-3.0"}

Davlan/xlm-roberta-large-ner-hrl

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us

Hugging Face's logo ------------------- language: * ar * de * en * es * fr * it * lv * nl * pt * zh * multilingual --- xlm-roberta-large-ner-hrl ========================= Model description ----------------- xlm-roberta-large-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa large model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 10 high-resourced languages Intended uses & limitations --------------------------- #### How to use You can use this model with Transformers *pipeline* for NER. #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Training data ------------- The training data for the 10 languages are from: The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Training procedure ------------------ This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.

text-generation

transformers

# Iron Man DialoGPT Model

{"tags": ["conversational"]}

Dawit/DialogGPT-small-ironman

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Iron Man DialoGPT Model

text-generation

transformers

# My Awesome Model

{"tags": ["conversational"]}

Daymarebait/Discord_BOT_RICK

2022-03-02T23:29:04+00:00

TAGS #transformers #conversational #endpoints_compatible #region-us

# My Awesome Model

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # emoBERTTamil This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tamilmixsentiment dataset. It achieves the following results on the evaluation set: - Loss: 0.9666 - Accuracy: 0.671 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1128 | 1.0 | 250 | 1.0290 | 0.672 | | 1.0226 | 2.0 | 500 | 1.0172 | 0.686 | | 0.9137 | 3.0 | 750 | 0.9666 | 0.671 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tamilmixsentiment"], "metrics": ["accuracy"], "model_index": [{"name": "emoBERTTamil", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "tamilmixsentiment", "type": "tamilmixsentiment", "args": "default"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.671}}]}]}

DeadBeast/emoBERTTamil

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-tamilmixsentiment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

emoBERTTamil ============ This model is a fine-tuned version of bert-base-uncased on the tamilmixsentiment dataset. It achieves the following results on the evaluation set: * Loss: 0.9666 * Accuracy: 0.671 Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.9.2 * Pytorch 1.9.0+cu102 * Datasets 1.11.0 * Tokenizers 0.10.3

text-classification

transformers

# **Korean-mBERT** This model is a fine-tune checkpoint of mBERT-base-cased over **Hugging Face Kore_Scm** dataset for Text classification. ### **How to use?** **Task**: binary-classification - LABEL_1: Sarcasm (*Sarcasm means tweets contains sarcasm*) - LABEL_0: Not Sarcasm (*Not Sarcasm means tweets do not contain sarcasm*) Click on **Use in Transformers**!

{"language": "korean", "license": "apache-2.0", "datasets": ["Korean-Sarcasm"]}

DeadBeast/korscm-mBERT

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #text-classification #dataset-Korean-Sarcasm #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Korean-mBERT This model is a fine-tune checkpoint of mBERT-base-cased over Hugging Face Kore_Scm dataset for Text classification. ### How to use? Task: binary-classification - LABEL_1: Sarcasm (*Sarcasm means tweets contains sarcasm*) - LABEL_0: Not Sarcasm (*Not Sarcasm means tweets do not contain sarcasm*) Click on Use in Transformers!

text-classification

transformers

# **mBERT-base-cased-finetuned-bengali-fakenews** This model is a fine-tune checkpoint of mBERT-base-cased over **[Bengali-fake-news Dataset](https://www.kaggle.com/cryptexcode/banfakenews)** for Text classification. This model reaches an accuracy of 96.3 with an f1-score of 79.1 on the dev set. ### **How to use?** **Task**: binary-classification - LABEL_1: Authentic (*Authentic means news is authentic*) - LABEL_0: Fake (*Fake means news is fake*) ``` from transformers import pipeline print(pipeline("sentiment-analysis",model="DeadBeast/mbert-base-cased-finetuned-bengali-fakenews",tokenizer="DeadBeast/mbert-base-cased-finetuned-bengali-fakenews")("অভিনেতা আফজাল শরীফকে ২০ লাখ টাকার অনুদান অসুস্থ অভিনেতা আফজাল শরীফকে চিকিৎসার জন্য ২০ লাখ টাকা অনুদান দিয়েছেন প্রধানমন্ত্রী শেখ হাসিনা।")) ```

{"language": "bengali", "license": "apache-2.0", "datasets": ["BanFakeNews"]}

DeadBeast/mbert-base-cased-finetuned-bengali-fakenews

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #text-classification #dataset-BanFakeNews #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# mBERT-base-cased-finetuned-bengali-fakenews This model is a fine-tune checkpoint of mBERT-base-cased over Bengali-fake-news Dataset for Text classification. This model reaches an accuracy of 96.3 with an f1-score of 79.1 on the dev set. ### How to use? Task: binary-classification - LABEL_1: Authentic (*Authentic means news is authentic*) - LABEL_0: Fake (*Fake means news is fake*)

--- Summarisation model summarsiation

{}

Dean/summarsiation

2022-03-02T23:29:04+00:00

TAGS #region-us

--- Summarisation model summarsiation

text-generation

transformers

#Scaramouche DialoGPT Model

{"tags": ["conversational"]}

DecafNosebleed/DialoGPT-small-ScaraBot

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#Scaramouche DialoGPT Model

transformers

# Model Card for ChemBERTa-10M-MTR # Model Details ## Model Description More information needed - **Developed by:** DeepChem - **Shared by [Optional]:** DeepChem - **Model type:** Token Classification - **Language(s) (NLP):** More information needed - **License:** More information needed - **Parent Model:** [RoBERTa](https://huggingface.co/roberta-base?text=The+goal+of+life+is+%3Cmask%3E.) - **Resources for more information:** More information needed # Uses ## Direct Use More information needed. ## Downstream Use [Optional] More information needed. ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data More information needed ## Training Procedure ### Preprocessing More information needed ### Speeds, Sizes, Times More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed ### Factors More information needed ### Metrics More information needed ## Results More information needed # Model Examination More information needed # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software More information needed. # Citation **BibTeX:** ```bibtex @book{Ramsundar-et-al-2019, title={Deep Learning for the Life Sciences}, author={Bharath Ramsundar and Peter Eastman and Patrick Walters and Vijay Pande and Karl Leswing and Zhenqin Wu}, publisher={O'Reilly Media}, note={\url{https://www.amazon.com/Deep-Learning-Life-Sciences-Microscopy/dp/1492039837}}, year={2019} } ``` **APA:** More information needed # Glossary [optional] More information needed # More Information [optional] More information needed # Model Card Authors [optional] DeepChem in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, RobertaForRegression tokenizer = AutoTokenizer.from_pretrained("DeepChem/ChemBERTa-10M-MTR") model = RobertaForRegression.from_pretrained("DeepChem/ChemBERTa-10M-MTR") ``` </details>

{"tags": ["roberta"]}

DeepChem/ChemBERTa-10M-MTR

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #arxiv-1910.09700 #endpoints_compatible #has_space #region-us

# Model Card for ChemBERTa-10M-MTR # Model Details ## Model Description More information needed - Developed by: DeepChem - Shared by [Optional]: DeepChem - Model type: Token Classification - Language(s) (NLP): More information needed - License: More information needed - Parent Model: RoBERTa - Resources for more information: More information needed # Uses ## Direct Use More information needed. ## Downstream Use [Optional] More information needed. ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data More information needed ## Training Procedure ### Preprocessing More information needed ### Speeds, Sizes, Times More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed ### Factors More information needed ### Metrics More information needed ## Results More information needed # Model Examination More information needed # Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: More information needed - Hours used: More information needed - Cloud Provider: More information needed - Compute Region: More information needed - Carbon Emitted: More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software More information needed. BibTeX: APA: More information needed # Glossary [optional] More information needed # More Information [optional] More information needed # Model Card Authors [optional] DeepChem in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> </details>

feature-extraction

transformers

RoBERTa model trained on 1M SMILES from PubChem 77M set in MoleculeNet. Uses Smiles-Tokenizer

{}

DeepChem/SmilesTokenizer_PubChem_1M

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us

RoBERTa model trained on 1M SMILES from PubChem 77M set in MoleculeNet. Uses Smiles-Tokenizer

text-generation

transformers

# GPT2-Spanish GPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the medium version of the original OpenAI GPT2 model. ## Corpus This model was trained with a corpus of 11.5GB of texts corresponding to 3.5GB of Wikipedia articles and 8GB of books (narrative, short stories, theater, poetry, essays, and popularization). ## Tokenizer The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for Unicode characters) and a vocabulary size of 50257. The inputs are sequences of 1024 consecutive tokens. This tokenizer was trained from scratch with the Spanish corpus, since it was evidenced that the tokenizer of the English models presented limitations to capture the semantic relations of Spanish, due to the morphosyntactic differences between both languages. Apart from the special token "<|endoftext|>" for text ending in the OpenAI GPT-2 models, the tokens "<|talk|>", "<|ax1|>", "<|ax2|>" (..)"<|ax9|>" were included so that they can serve as prompts in future training. ## Training The model and tokenizer were trained using the Hugging Face libraries with an Nvidia Tesla V100 GPU with 16GB memory on Google Colab servers. ## Authors The model was trained by Alejandro Oñate Latorre (Spain) and Jorge Ortiz Fuentes (Chile), members of -Deep ESP-, an open-source community on Natural Language Processing in Spanish (https://t.me/joinchat/VoEp1bPrDYEexc6h). Thanks to the members of the community who collaborated with funding for the initial tests. ## Cautions The model generates texts according to the patterns learned in the training corpus. These data were not filtered, therefore, the model could generate offensive or discriminatory content.

{"language": "es", "license": "mit", "tags": ["GPT-2", "Spanish", "ebooks", "nlg"], "datasets": ["ebooks"], "widget": [{"text": "Quisiera saber que va a suceder"}]}

DeepESP/gpt2-spanish-medium

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #jax #gpt2 #text-generation #GPT-2 #Spanish #ebooks #nlg #es #dataset-ebooks #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# GPT2-Spanish GPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the medium version of the original OpenAI GPT2 model. ## Corpus This model was trained with a corpus of 11.5GB of texts corresponding to 3.5GB of Wikipedia articles and 8GB of books (narrative, short stories, theater, poetry, essays, and popularization). ## Tokenizer The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for Unicode characters) and a vocabulary size of 50257. The inputs are sequences of 1024 consecutive tokens. This tokenizer was trained from scratch with the Spanish corpus, since it was evidenced that the tokenizer of the English models presented limitations to capture the semantic relations of Spanish, due to the morphosyntactic differences between both languages. Apart from the special token "<|endoftext|>" for text ending in the OpenAI GPT-2 models, the tokens "<|talk|>", "<|ax1|>", "<|ax2|>" (..)"<|ax9|>" were included so that they can serve as prompts in future training. ## Training The model and tokenizer were trained using the Hugging Face libraries with an Nvidia Tesla V100 GPU with 16GB memory on Google Colab servers. ## Authors The model was trained by Alejandro Oñate Latorre (Spain) and Jorge Ortiz Fuentes (Chile), members of -Deep ESP-, an open-source community on Natural Language Processing in Spanish (https://t.me/joinchat/VoEp1bPrDYEexc6h). Thanks to the members of the community who collaborated with funding for the initial tests. ## Cautions The model generates texts according to the patterns learned in the training corpus. These data were not filtered, therefore, the model could generate offensive or discriminatory content.

text-generation

transformers

# GPT2-Spanish GPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the small version of the original OpenAI GPT2 model. ## Corpus This model was trained with a corpus of 11.5GB of texts corresponding to 3.5GB of Wikipedia articles and 8GB of books (narrative, short stories, theater, poetry, essays, and popularization). ## Tokenizer The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for Unicode characters) and a vocabulary size of 50257. The inputs are sequences of 1024 consecutive tokens. This tokenizer was trained from scratch with the Spanish corpus, since it was evidenced that the tokenizer of the English models presented limitations to capture the semantic relations of Spanish, due to the morphosyntactic differences between both languages. Apart from the special token "<|endoftext|>" for text ending in the OpenAI GPT-2 models, the tokens "<|talk|>", "<|ax1|>", "<|ax2|>" (..)"<|ax9|>" were included so that they can serve as prompts in future training. ## Training The model and tokenizer were trained using the Hugging Face libraries with an Nvidia Tesla V100 GPU with 16GB memory on Google Colab servers. ## Authors The model was trained by Alejandro Oñate Latorre (Spain) and Jorge Ortiz Fuentes (Chile), members of -Deep ESP-, an open-source community on Natural Language Processing in Spanish (https://t.me/joinchat/VoEp1bPrDYEexc6h). Thanks to the members of the community who collaborated with funding for the initial tests. ## Cautions The model generates texts according to the patterns learned in the training corpus. These data were not filtered, therefore, the model could generate offensive or discriminatory content.

{"language": "es", "license": "mit", "tags": ["GPT-2", "Spanish", "ebooks", "nlg"], "datasets": ["ebooks"], "widget": [{"text": "Quisiera saber que va a suceder"}]}

DeepESP/gpt2-spanish

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tf #jax #gpt2 #text-generation #GPT-2 #Spanish #ebooks #nlg #es #dataset-ebooks #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us

# GPT2-Spanish GPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the small version of the original OpenAI GPT2 model. ## Corpus This model was trained with a corpus of 11.5GB of texts corresponding to 3.5GB of Wikipedia articles and 8GB of books (narrative, short stories, theater, poetry, essays, and popularization). ## Tokenizer The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for Unicode characters) and a vocabulary size of 50257. The inputs are sequences of 1024 consecutive tokens. This tokenizer was trained from scratch with the Spanish corpus, since it was evidenced that the tokenizer of the English models presented limitations to capture the semantic relations of Spanish, due to the morphosyntactic differences between both languages. Apart from the special token "<|endoftext|>" for text ending in the OpenAI GPT-2 models, the tokens "<|talk|>", "<|ax1|>", "<|ax2|>" (..)"<|ax9|>" were included so that they can serve as prompts in future training. ## Training The model and tokenizer were trained using the Hugging Face libraries with an Nvidia Tesla V100 GPU with 16GB memory on Google Colab servers. ## Authors The model was trained by Alejandro Oñate Latorre (Spain) and Jorge Ortiz Fuentes (Chile), members of -Deep ESP-, an open-source community on Natural Language Processing in Spanish (https://t.me/joinchat/VoEp1bPrDYEexc6h). Thanks to the members of the community who collaborated with funding for the initial tests. ## Cautions The model generates texts according to the patterns learned in the training corpus. These data were not filtered, therefore, the model could generate offensive or discriminatory content.

feature-extraction

transformers

# bert-base-bg-cs-pl-ru-cased SlavicBERT\[1\] \(Slavic \(bg, cs, pl, ru\), cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on Russian News and four Wikipedias: Bulgarian, Czech, Polish, and Russian. Subtoken vocabulary was built using this data. Multilingual BERT was used as an initialization for SlavicBERT. 08.11.2021: upload model with MLM and NSP heads \[1\]: Arkhipov M., Trofimova M., Kuratov Y., Sorokin A. \(2019\). [Tuning Multilingual Transformers for Language-Specific Named Entity Recognition](https://www.aclweb.org/anthology/W19-3712/). ACL anthology W19-3712.

{"language": ["bg", "cs", "pl", "ru"]}

DeepPavlov/bert-base-bg-cs-pl-ru-cased

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #feature-extraction #bg #cs #pl #ru #endpoints_compatible #region-us

# bert-base-bg-cs-pl-ru-cased SlavicBERT\[1\] \(Slavic \(bg, cs, pl, ru\), cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on Russian News and four Wikipedias: Bulgarian, Czech, Polish, and Russian. Subtoken vocabulary was built using this data. Multilingual BERT was used as an initialization for SlavicBERT. 08.11.2021: upload model with MLM and NSP heads \[1\]: Arkhipov M., Trofimova M., Kuratov Y., Sorokin A. \(2019\). Tuning Multilingual Transformers for Language-Specific Named Entity Recognition. ACL anthology W19-3712.

feature-extraction

transformers

# bert-base-cased-conversational Conversational BERT \(English, cased, 12‑layer, 768‑hidden, 12‑heads, 110M parameters\) was trained on the English part of Twitter, Reddit, DailyDialogues\[1\], OpenSubtitles\[2\], Debates\[3\], Blogs\[4\], Facebook News Comments. We used this training data to build the vocabulary of English subtokens and took English cased version of BERT‑base as an initialization for English Conversational BERT. 08.11.2021: upload model with MLM and NSP heads \[1\]: Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset. IJCNLP 2017. \[2\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\) \[3\]: Justine Zhang, Ravi Kumar, Sujith Ravi, Cristian Danescu-Niculescu-Mizil. Proceedings of NAACL, 2016. \[4\]: J. Schler, M. Koppel, S. Argamon and J. Pennebaker \(2006\). Effects of Age and Gender on Blogging in Proceedings of 2006 AAAI Spring Symposium on Computational Approaches for Analyzing Weblogs.

{"language": "en"}

DeepPavlov/bert-base-cased-conversational

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #feature-extraction #en #endpoints_compatible #region-us

# bert-base-cased-conversational Conversational BERT \(English, cased, 12‑layer, 768‑hidden, 12‑heads, 110M parameters\) was trained on the English part of Twitter, Reddit, DailyDialogues\[1\], OpenSubtitles\[2\], Debates\[3\], Blogs\[4\], Facebook News Comments. We used this training data to build the vocabulary of English subtokens and took English cased version of BERT‑base as an initialization for English Conversational BERT. 08.11.2021: upload model with MLM and NSP heads \[1\]: Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset. IJCNLP 2017. \[2\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\) \[3\]: Justine Zhang, Ravi Kumar, Sujith Ravi, Cristian Danescu-Niculescu-Mizil. Proceedings of NAACL, 2016. \[4\]: J. Schler, M. Koppel, S. Argamon and J. Pennebaker \(2006\). Effects of Age and Gender on Blogging in Proceedings of 2006 AAAI Spring Symposium on Computational Approaches for Analyzing Weblogs.

feature-extraction

transformers

# bert-base-multilingual-cased-sentence Sentence Multilingual BERT \(101 languages, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) is a representation‑based sentence encoder for 101 languages of Multilingual BERT. It is initialized with Multilingual BERT and then fine‑tuned on english MultiNLI\[1\] and on dev set of multilingual XNLI\[2\]. Sentence representations are mean pooled token embeddings in the same manner as in Sentence‑BERT\[3\]. \[1\]: Williams A., Nangia N. & Bowman S. \(2017\) A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. arXiv preprint [arXiv:1704.05426](https://arxiv.org/abs/1704.05426) \[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053) \[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)

{"language": ["multilingual"]}

DeepPavlov/bert-base-multilingual-cased-sentence

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #feature-extraction #multilingual #arxiv-1704.05426 #arxiv-1809.05053 #arxiv-1908.10084 #endpoints_compatible #region-us

# bert-base-multilingual-cased-sentence Sentence Multilingual BERT \(101 languages, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) is a representation‑based sentence encoder for 101 languages of Multilingual BERT. It is initialized with Multilingual BERT and then fine‑tuned on english MultiNLI\[1\] and on dev set of multilingual XNLI\[2\]. Sentence representations are mean pooled token embeddings in the same manner as in Sentence‑BERT\[3\]. \[1\]: Williams A., Nangia N. & Bowman S. \(2017\) A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. arXiv preprint arXiv:1704.05426 \[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint arXiv:1809.05053 \[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint arXiv:1908.10084

transformers

# distilrubert-base-cased-conversational Conversational DistilRuBERT \(Russian, cased, 6‑layer, 768‑hidden, 12‑heads, 135.4M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversational RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational)). Our DistilRuBERT was highly inspired by \[3\], \[4\]. Namely, we used * KL loss (between teacher and student output logits) * MLM loss (between tokens labels and student output logits) * Cosine embedding loss between mean of two consecutive hidden states of the teacher and one hidden state of the student The model was trained for about 100 hrs. on 8 nVIDIA Tesla P100-SXM2.0 16Gb. To evaluate improvements in the inference speed, we ran teacher and student models on random sequences with seq_len=512, batch_size = 16 (for throughput) and batch_size=1 (for latency). All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA Tesla P100-SXM2.0 16Gb. | Model | Size, Mb. | CPU latency, sec.| GPU latency, sec. | CPU throughput, samples/sec. | GPU throughput, samples/sec. | |-------------------------------------------------|------------|------------------|-------------------|------------------------------|------------------------------| | Teacher (RuBERT-base-cased-conversational) | 679 | 0.655 | 0.031 | 0.3754 | 36.4902 | | Student (DistilRuBERT-base-cased-conversational)| 517 | 0.3285 | 0.0212 | 0.5803 | 52.2495 | # Citation If you found the model useful for your research, we are kindly ask to cite [this](https://arxiv.org/abs/2205.02340) paper: ``` @misc{https://doi.org/10.48550/arxiv.2205.02340, doi = {10.48550/ARXIV.2205.02340}, url = {https://arxiv.org/abs/2205.02340}, author = {Kolesnikova, Alina and Kuratov, Yuri and Konovalov, Vasily and Burtsev, Mikhail}, keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Knowledge Distillation of Russian Language Models with Reduction of Vocabulary}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` \[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\) \[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017. \[3\]: Sanh, V., Debut, L., Chaumond, J., & Wolf, T. \(2019\). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. \[4\]: <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>

{"language": ["ru"]}

DeepPavlov/distilrubert-base-cased-conversational

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #ru #arxiv-2205.02340 #endpoints_compatible #region-us

distilrubert-base-cased-conversational ====================================== Conversational DistilRuBERT (Russian, cased, 6‑layer, 768‑hidden, 12‑heads, 135.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT). Our DistilRuBERT was highly inspired by [3], [4]. Namely, we used * KL loss (between teacher and student output logits) * MLM loss (between tokens labels and student output logits) * Cosine embedding loss between mean of two consecutive hidden states of the teacher and one hidden state of the student The model was trained for about 100 hrs. on 8 nVIDIA Tesla P100-SXM2.0 16Gb. To evaluate improvements in the inference speed, we ran teacher and student models on random sequences with seq\_len=512, batch\_size = 16 (for throughput) and batch\_size=1 (for latency). All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA Tesla P100-SXM2.0 16Gb. If you found the model useful for your research, we are kindly ask to cite this paper: [1]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) [2]: Shavrina T., Shapovalova O. (2017) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017. [3]: Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. [4]: <URL