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beston91/gpt2-xl_ft_mult_25k
299c428a79c44d3abac41da6785f17401ee10ee7
2022-03-27T17:02:18.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "model-index" ]
text-generation
false
beston91
null
beston91/gpt2-xl_ft_mult_25k
5
null
transformers
17,000
--- tags: - generated_from_trainer model-index: - name: gpt2-xl_ft_mult_25k results: [] --- <!-- 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. --> # gpt2-xl_ft_mult_25k This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5782 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100.0 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.99 | 136 | 0.6434 | | No log | 1.99 | 272 | 0.5941 | | No log | 2.99 | 408 | 0.5811 | | 1.1604 | 3.99 | 544 | 0.5782 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6 ### Perplexity Score: 47.53093719482422
ahmeddbahaa/mt5-small-finetuned-mt5-en
fe112242e8a3553368958fd314e798e763ec4581
2022-03-24T20:02:45.000Z
[ "pytorch", "tensorboard", "mt5", "text2text-generation", "dataset:xlsum", "transformers", "summarization", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
summarization
false
ahmeddbahaa
null
ahmeddbahaa/mt5-small-finetuned-mt5-en
5
null
transformers
17,001
--- license: apache-2.0 tags: - summarization - generated_from_trainer datasets: - xlsum metrics: - rouge model-index: - name: mt5-small-finetuned-mt5-en results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xlsum type: xlsum args: english metrics: - name: Rouge1 type: rouge value: 23.8952 --- <!-- 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. --> # mt5-small-finetuned-mt5-en This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 2.8345 - Rouge1: 23.8952 - Rouge2: 5.8792 - Rougel: 18.6495 - Rougelsum: 18.7057 ## 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: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 10 - total_train_batch_size: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | No log | 1.0 | 224 | 3.0150 | 24.4639 | 5.3016 | 18.3987 | 18.4963 | | No log | 2.0 | 448 | 2.8738 | 24.5075 | 5.842 | 18.8133 | 18.9072 | | No log | 3.0 | 672 | 2.8345 | 23.8952 | 5.8792 | 18.6495 | 18.7057 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
hackathon-pln-es/class-poems-es
f4538cce6a98bd55e575169d3d0d8939ddcd716f
2022-03-28T16:11:33.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
hackathon-pln-es
null
hackathon-pln-es/class-poems-es
5
4
transformers
17,002
--- license: apache-2.0 tags: - generated_from_trainer widget: - text: "El amor es una experiencia universal que nos conmueve a todos, pero a veces no hallamos las palabras adecuadas para expresarlo. A lo largo de la historia los poetas han sabido decir aquello que todos sentimos de formas creativas y elocuentes." - text: "Había un hombre a quien la Pena nombraba su amigo, Y él, soñando con su gran camarada la Pena, Iba andando con paso lento por las arenas resplandecientes Y zumbantes, donde van oleajes ventosos: Y llamó en voz alta a las estrellas para que se inclinaran Desde sus pálidos tronos. y lo consuelan, pero entre ellos se ríen y cantan siempre: Y entonces el hombre a quien la Tristeza nombró su amigo Gritó, ¡Mar oscuro, escucha mi más lastimosa historia! El mar avanzaba y seguía gritando su viejo grito, rodando en sueños de colina en colina. Huyó de la persecución de su gloria Y, en un valle lejano y apacible deteniéndose, Gritó toda su historia a las gotas de rocío que brillan. Pero nada oyeron, porque siempre están escuchando, Las gotas de rocío, por el sonido de su propio goteo. Y entonces el hombre a quien Triste nombró su amigo Buscó una vez más la orilla, y encontró una concha, Y pensó: Contaré mi pesada historia Hasta que mis propias palabras, resonando, envíen Su tristeza a través de un corazón hueco y perlado; Y mi propia historia volverá a cantar para mí, Y mis propias palabras susurrantes serán de consuelo, ¡Y he aquí! mi antigua carga puede partir. Luego cantó suavemente cerca del borde nacarado; Pero el triste habitante de los caminos marítimos solitarios Cambió todo lo que cantaba en un gemido inarticulado Entre sus torbellinos salvajes, olvidándolo." - text: "Ven, ven, muerte, Y en triste ciprés déjame descansar. Vuela lejos, vuela lejos, respira; Soy asesinado por una bella y cruel doncella. Mi sudario de blanco, pegado todo con tejo, ¡Oh, prepáralo! Mi parte de la muerte, nadie tan fiel la compartió. Ni una flor, ni una flor dulce, En mi ataúd negro que se desparrame. Ni un amigo, ni un amigo saludan Mi pobre cadáver, donde mis huesos serán arrojados. Mil mil suspiros para salvar, Acuéstame, oh, donde Triste amante verdadero nunca encuentre mi tumba, ¡Para llorar allí!" metrics: - accuracy model-index: - name: classification-poems results: [] --- <!-- 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. --> # classification-poems This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the spanish Poems Dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.8228 - Accuracy: 0.7241 ## Model description The model was trained to classify poems in Spanish, taking into account the content. ## Training and evaluation data The original dataset has the columns author, content, title, year and type of poem. For each example, the type of poem it belongs to is identified. Then the model will recognize which type of poem the entered content belongs to. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9344 | 1.0 | 258 | 0.7505 | 0.7586 | | 0.9239 | 2.0 | 516 | 0.8228 | 0.7241 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
j-hartmann/concreteness-english-distilroberta-base
b8c52ae8a72378a15322b57bb0888c9be9161683
2022-03-25T10:03:11.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
j-hartmann
null
j-hartmann/concreteness-english-distilroberta-base
5
null
transformers
17,003
"Concreteness evaluates the degree to which the concept denoted by a word refers to a perceptible entity." (Brysbaert, Warriner, and Kuperman 2014, p. 904)
Jingya/t5-large-finetuned-xsum
963264e08ba991e4201d10c14650ef1a880ef565
2022-03-25T16:15:09.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Jingya
null
Jingya/t5-large-finetuned-xsum
5
null
transformers
17,004
Entry not found
UWB-AIR/MQDD-duplicates
fb3f52f1c10cf2d44e8bf28bc55f34b4a193f450
2022-04-05T06:24:29.000Z
[ "pytorch", "longformer", "feature-extraction", "arxiv:2203.14093", "transformers", "license:cc-by-nc-sa-4.0" ]
feature-extraction
false
UWB-AIR
null
UWB-AIR/MQDD-duplicates
5
null
transformers
17,005
--- license: cc-by-nc-sa-4.0 --- # MQDD - Multimodal Question Duplicity Detection This repository publishes trained models and other supporting materials for the paper [MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain](https://arxiv.org/abs/2203.14093). For more information, see the paper. The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our [Stack Overflow Dataset repository](https://github.com/kiv-air/StackOverflowDataset). To acquire the pre-trained model only, see the [UWB-AIR/MQDD-pretrained](https://huggingface.co/UWB-AIR/MQDD-pretrained). ## Fine-tuned MQDD We release a fine-tuned version of our MQDD model for duplicate detection task. The model's architecture follows the architecture of a two-tower model as depicted in the figure below: <img src="https://raw.githubusercontent.com/kiv-air/MQDD/master/img/architecture.png" width="700"> A self-standing encoder without a duplicate detection head can be loaded using the following source code snippet. Such a model can be used for building search systems based, for example, on [Faiss](https://github.com/facebookresearch/faiss) library. ```Python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("UWB-AIR/MQDD-duplicates") model = AutoModel.from_pretrained("UWB-AIR/MQDD-duplicates") ``` A checkpoint of a full two-tower model can than be obtained from our [GoogleDrive folder](https://drive.google.com/drive/folders/1CYiqF2GJ2fSQzx_oM4-X_IhpObi4af5Q?usp=sharing). To load the model, one needs to use the model's implementation from `models/MQDD_model.py` in our [GitHub repository](https://github.com/kiv-air/MQDD). To construct the model and load it's checkpoint, use the following source code: ```Python from MQDD_model import ClsHeadModelMQDD model = ClsHeadModelMQDD("UWB-AIR/MQDD-duplicates") ckpt = torch.load("model.pt", map_location="cpu") model.load_state_dict(ckpt["model_state"]) ``` ## Licence This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/ ## How should I cite the MQDD? For now, please cite [the Arxiv paper](https://arxiv.org/abs/2203.14093): ``` @misc{https://doi.org/10.48550/arxiv.2203.14093, doi = {10.48550/ARXIV.2203.14093}, url = {https://arxiv.org/abs/2203.14093}, author = {Pašek, Jan and Sido, Jakub and Konopík, Miloslav and Pražák, Ondřej}, title = {MQDD -- Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International} } ```
nairaxo/dev-multi
470d815aaaa28b7a5738d1c954d11605fd5a849c
2022-07-11T11:16:51.000Z
[ "wav2vec2", "feature-extraction", "multilingual", "dataset:commonvoice", "speechbrain", "CTC", "pytorch", "Transformer", "license:apache-2.0", "automatic-speech-recognition" ]
automatic-speech-recognition
false
nairaxo
null
nairaxo/dev-multi
5
null
speechbrain
17,006
--- language: multilingual thumbnail: pipeline_tag: automatic-speech-recognition tags: - CTC - pytorch - speechbrain - Transformer license: "apache-2.0" datasets: - commonvoice metrics: - wer - cer --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # wav2vec 2.0 with CTC/Attention trained on multilingual African dataset This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on a multilingual African dataset. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). | Dataset Link | Language | Test WER | |:-----------------:| -----:| -----:| | [DVoice](https://zenodo.org/record/6342622) | Darija | 13.27 | | [DVoice/VoxLingua107](https://zenodo.org/record/6342622) + [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) | Swahili | 29.31 | | [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) | Fongbe | 10.26 | | [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) | Wolof | 21.54 | | [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) | Amharic | 31.15 | # About DVoice DVoice is a community initiative that aims to provide African languages and dialects with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each language. Two different approaches are currently used: the DVoice platforms ([https://dvoice.ma](https://dvoice.ma) and [https://dvoice.sn](https://dvoice.sn)), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling the recordings. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola and Soninke. ## Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions. - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on the Darija dataset. The obtained final acoustic representation is given to the CTC greedy decoder. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed. ## Install SpeechBrain First of all, please install tranformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` Please notice that we encourage you to read the SpeechBrain tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Transcribing your own audio files ```python from speechbrain.pretrained import EncoderASR asr_model = EncoderASR.from_hparams(source="nairaxo/dvoice-multilingual", savedir="pretrained_models/asr-wav2vec2-dvoice-multi") asr_model.transcribe_file('./the_path_to_your_audio_file') ``` ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ### Training To train the model from scratch, please see our GitHub tutorial [here](https://github.com/AIOXLABS/DVoice). ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` #### About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain
emre/java-RoBERTa-Tara-small
855a899d8860e0c60fed0d14ba9e4da9c5354d8b
2022-03-27T21:19:45.000Z
[ "pytorch", "roberta", "fill-mask", "java", "code", "dataset:code_search_net", "transformers", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
emre
null
emre/java-RoBERTa-Tara-small
5
1
transformers
17,007
--- language: - java - code license: apache-2.0 datasets: - code_search_net widget: - text: 'public <mask> isOdd(Integer num){if (num % 2 == 0) {return "even";} else {return "odd";}}' --- ## JavaRoBERTa-Tara A RoBERTa model pretrained on, code_search_net Java software code. ### Training Data The model was trained on 10,223,695 Java files retrieved from open source projects on GitHub. ### Training Objective A MLM (Masked Language Model) objective was used to train this model. ### Usage ```python from transformers import pipeline pipe = pipeline('fill-mask', model='emre/java-RoBERTa-Tara-small') output = pipe(CODE) # Replace with Java code; Use '<mask>' to mask tokens/words in the code. ``` ### Why Tara? she is the name of my little baby girl :)
PaddyP/distilbert-base-uncased-finetuned-emotion
55a5ac830de049e65cb30f645d95aebfba81eeb5
2022-03-27T07:06:37.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
PaddyP
null
PaddyP/distilbert-base-uncased-finetuned-emotion
5
null
transformers
17,008
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- 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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2302 - Accuracy: 0.922 - F1: 0.9218 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3344 | 0.903 | 0.9004 | | No log | 2.0 | 500 | 0.2302 | 0.922 | 0.9218 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1 - Datasets 2.0.0 - Tokenizers 0.10.3
ScandinavianMrT/distilbert_ONION_1epoch_3.0
8f66a1d6c168fa151bddca8f3c42b9b06b2fa757
2022-03-27T08:00:18.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
ScandinavianMrT
null
ScandinavianMrT/distilbert_ONION_1epoch_3.0
5
null
transformers
17,009
Entry not found
hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd
7127d605e834e22c1cadc70a85f930c94c6e548b
2022-04-04T02:40:54.000Z
[ "pytorch", "tensorboard", "wav2vec2", "audio-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
audio-classification
false
hackathon-pln-es
null
hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd
5
4
transformers
17,010
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-sentiment-mesd results: [] --- # wav2vec2-base-finetuned-sentiment-mesd This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [MESD](https://huggingface.co/hackathon-pln-es/MESD) dataset. It achieves the following results on the evaluation set: - Loss: 0.5729 - Accuracy: 0.8308 ## Model description This model was trained to classify underlying sentiment of Spanish audio/speech. ## 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: 1.25e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 7 | 0.5729 | 0.8308 | | No log | 2.0 | 14 | 0.6577 | 0.8 | | 0.1602 | 3.0 | 21 | 0.7055 | 0.8 | | 0.1602 | 4.0 | 28 | 0.8696 | 0.7615 | | 0.1602 | 5.0 | 35 | 0.6807 | 0.7923 | | 0.1711 | 6.0 | 42 | 0.7303 | 0.7923 | | 0.1711 | 7.0 | 49 | 0.7028 | 0.8077 | | 0.1711 | 8.0 | 56 | 0.7368 | 0.8 | | 0.1608 | 9.0 | 63 | 0.7190 | 0.7923 | | 0.1608 | 10.0 | 70 | 0.6913 | 0.8077 | | 0.1608 | 11.0 | 77 | 0.7047 | 0.8077 | | 0.1753 | 12.0 | 84 | 0.6801 | 0.8 | | 0.1753 | 13.0 | 91 | 0.7208 | 0.7769 | | 0.1753 | 14.0 | 98 | 0.7458 | 0.7846 | | 0.203 | 15.0 | 105 | 0.6494 | 0.8077 | | 0.203 | 16.0 | 112 | 0.6256 | 0.8231 | | 0.203 | 17.0 | 119 | 0.6788 | 0.8 | | 0.1919 | 18.0 | 126 | 0.6757 | 0.7846 | | 0.1919 | 19.0 | 133 | 0.6859 | 0.7846 | | 0.1641 | 20.0 | 140 | 0.6832 | 0.7846 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.10.3
Jiexing/sparc_relation_t5_3b-2432
b144117ea15f3f88f1f0e20acc860179a66c86dd
2022-03-27T14:38:28.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Jiexing
null
Jiexing/sparc_relation_t5_3b-2432
5
null
transformers
17,011
Entry not found
tau/pegasus_4_1024_0.3_epoch1
d57c5bc093fc4ceab75c58da7d78c05734c2c1c7
2022-03-28T04:32:24.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
tau
null
tau/pegasus_4_1024_0.3_epoch1
5
null
transformers
17,012
Entry not found
GleamEyeBeast/ascend
52c68be2caaaed0f907e2f0aa23db8f15f47198b
2022-03-29T16:49:48.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "model-index" ]
automatic-speech-recognition
false
GleamEyeBeast
null
GleamEyeBeast/ascend
5
null
transformers
17,013
--- tags: - generated_from_trainer model-index: - name: ascend results: [] --- <!-- 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. --> # ascend This model is a fine-tuned version of [GleamEyeBeast/ascend](https://huggingface.co/GleamEyeBeast/ascend) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3718 - Wer: 0.6412 - Cer: 0.2428 ## 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: 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: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 0.5769 | 1.0 | 688 | 1.1864 | 0.7716 | 0.3159 | | 0.5215 | 2.0 | 1376 | 1.1613 | 0.7504 | 0.2965 | | 0.4188 | 3.0 | 2064 | 1.1644 | 0.7389 | 0.2950 | | 0.3695 | 4.0 | 2752 | 1.1937 | 0.7184 | 0.2815 | | 0.3404 | 5.0 | 3440 | 1.1947 | 0.7083 | 0.2719 | | 0.2885 | 6.0 | 4128 | 1.2314 | 0.7108 | 0.2685 | | 0.2727 | 7.0 | 4816 | 1.2243 | 0.6850 | 0.2616 | | 0.2417 | 8.0 | 5504 | 1.2506 | 0.6767 | 0.2608 | | 0.2207 | 9.0 | 6192 | 1.2804 | 0.6922 | 0.2595 | | 0.2195 | 10.0 | 6880 | 1.2582 | 0.6818 | 0.2575 | | 0.1896 | 11.0 | 7568 | 1.3101 | 0.6814 | 0.2545 | | 0.1961 | 12.0 | 8256 | 1.2793 | 0.6706 | 0.2526 | | 0.1752 | 13.0 | 8944 | 1.2643 | 0.6584 | 0.2509 | | 0.1638 | 14.0 | 9632 | 1.3152 | 0.6588 | 0.2482 | | 0.1522 | 15.0 | 10320 | 1.3098 | 0.6433 | 0.2439 | | 0.1351 | 16.0 | 11008 | 1.3253 | 0.6537 | 0.2447 | | 0.1266 | 17.0 | 11696 | 1.3394 | 0.6365 | 0.2418 | | 0.1289 | 18.0 | 12384 | 1.3718 | 0.6412 | 0.2443 | | 0.1204 | 19.0 | 13072 | 1.3708 | 0.6433 | 0.2433 | | 0.1189 | 20.0 | 13760 | 1.3718 | 0.6412 | 0.2428 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
abdusahmbzuai/aradia-class-v1
c284997a458940783dba46514655310bc2226f71
2022-04-04T10:01:57.000Z
[ "pytorch", "wav2vec2", "audio-classification", "transformers" ]
audio-classification
false
abdusahmbzuai
null
abdusahmbzuai/aradia-class-v1
5
null
transformers
17,014
Entry not found
Cheatham/xlm-roberta-large-finetuned-d1-003
7892085ce6e8a9763a6c238da6436a65aa260014
2022-03-30T15:15:42.000Z
[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]
text-classification
false
Cheatham
null
Cheatham/xlm-roberta-large-finetuned-d1-003
5
null
transformers
17,015
Entry not found
hackathon-pln-es/readability-es-3class-sentences
0ec8dc6118476746de1e99fc61790f5d18ad6404
2022-04-04T10:41:57.000Z
[ "pytorch", "roberta", "text-classification", "es", "transformers", "spanish", "bertin", "license:cc-by-4.0" ]
text-classification
false
hackathon-pln-es
null
hackathon-pln-es/readability-es-3class-sentences
5
2
transformers
17,016
--- language: es license: cc-by-4.0 tags: - spanish - roberta - bertin pipeline_tag: text-classification widget: - text: Las Líneas de Nazca son una serie de marcas trazadas en el suelo, cuya anchura oscila entre los 40 y los 110 centímetros. - text: Hace mucho tiempo, en el gran océano que baña las costas del Perú no había peces. --- # Readability ES Sentences for three classes Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts. ## Description and performance This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs classification among three complexity levels: - Basic. - Intermediate. - Advanced. The relationship of these categories with the Common European Framework of Reference for Languages is described in [our report](https://wandb.ai/readability-es/readability-es/reports/Texts-Readability-Analysis-for-Spanish--VmlldzoxNzU2MDUx). This model achieves a F1 macro average score of 0.6951, measured on the validation set. ## Model variants - [`readability-es-sentences`](https://huggingface.co/hackathon-pln-es/readability-es-sentences). Two classes, sentence-based dataset. - [`readability-es-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-paragraphs). Two classes, paragraph-based dataset. - `readability-es-3class-sentences` (this model). Three classes, sentence-based dataset. - [`readability-es-3class-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-3class-paragraphs). Three classes, paragraph-based dataset. ## Datasets - [`readability-es-hackathon-pln-public`](https://huggingface.co/datasets/hackathon-pln-es/readability-es-hackathon-pln-public), composed of: * coh-metrix-esp corpus. * Various text resources scraped from websites. - Other non-public datasets: newsela-es, simplext. ## Training details Please, refer to [this training run](https://wandb.ai/readability-es/readability-es/runs/1qe3kbqj/overview) for full details on hyperparameters and training regime. ## Biases and Limitations - Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set. - One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases. - Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes. - Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented. - No effort has been performed to alleviate the shortcomings and biases described in the [original implementation of BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish#bias-examples-spanish). ## Authors - [Laura Vásquez-Rodríguez](https://lmvasque.github.io/) - [Pedro Cuenca](https://twitter.com/pcuenq) - [Sergio Morales](https://www.fireblend.com/) - [Fernando Alva-Manchego](https://feralvam.github.io/)
Tahsin-Mayeesha/distilbert-finetuned-fakenews
3e92f83efc6827517f030b648da21b9fceb2b2c3
2022-03-31T17:11:42.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
Tahsin-Mayeesha
null
Tahsin-Mayeesha/distilbert-finetuned-fakenews
5
null
transformers
17,017
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-finetuned-fakenews results: [] --- <!-- 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-finetuned-fakenews This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0049 - Accuracy: 0.9995 - F1: 0.9995 ## 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0392 | 1.0 | 500 | 0.0059 | 0.999 | 0.999 | | 0.002 | 2.0 | 1000 | 0.0047 | 0.9995 | 0.9995 | | 0.0001 | 3.0 | 1500 | 0.0047 | 0.9995 | 0.9995 | | 0.0001 | 4.0 | 2000 | 0.0049 | 0.9995 | 0.9995 | | 0.0 | 5.0 | 2500 | 0.0049 | 0.9995 | 0.9995 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.12.0
redwoodresearch/injuriousness-classifier-29apr-manual
f8b86553c0239c772bcf977d6ad0544ddab3ab06
2022-03-31T17:28:22.000Z
[ "pytorch", "deberta-v2", "text-classification", "transformers" ]
text-classification
false
redwoodresearch
null
redwoodresearch/injuriousness-classifier-29apr-manual
5
null
transformers
17,018
Entry not found
redwoodresearch/injuriousness-classifier-29apr-paraphrases
8f9ccede7579beff67893f01fb865a483765f0d5
2022-03-31T17:32:36.000Z
[ "pytorch", "deberta-v2", "text-classification", "transformers" ]
text-classification
false
redwoodresearch
null
redwoodresearch/injuriousness-classifier-29apr-paraphrases
5
null
transformers
17,019
Entry not found
Aymene/Fake-news-detection-bert-based-uncased
5857d13433586740c21238361937bf53920a5667
2022-04-02T02:42:06.000Z
[ "pytorch", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
Aymene
null
Aymene/Fake-news-detection-bert-based-uncased
5
null
transformers
17,020
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Fake-news-detection-bert-based-uncased results: [] --- <!-- 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. --> # Fake-news-detection-bert-based-uncased This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. ## 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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Tokenizers 0.11.6
RupaliP/wikineural-multilingual-ner
e636179e65ea9c5a0d7aa0d7e8d2c2260a5a0787
2022-04-11T11:54:43.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
RupaliP
null
RupaliP/wikineural-multilingual-ner
5
null
transformers
17,021
Entry not found
mp6kv/paper_feedback_intent
fa9268761b8245186eb34356ae3a282d34036e09
2022-04-02T21:42:38.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
mp6kv
null
mp6kv/paper_feedback_intent
5
null
transformers
17,022
--- license: mit tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: paper_feedback_intent results: [] --- <!-- 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. --> # paper_feedback_intent This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3621 - Accuracy: 0.9302 - Precision: 0.9307 - Recall: 0.9302 - F1: 0.9297 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9174 | 1.0 | 11 | 0.7054 | 0.7907 | 0.7903 | 0.7907 | 0.7861 | | 0.6917 | 2.0 | 22 | 0.4665 | 0.8140 | 0.8134 | 0.8140 | 0.8118 | | 0.4276 | 3.0 | 33 | 0.3326 | 0.9070 | 0.9065 | 0.9070 | 0.9041 | | 0.2656 | 4.0 | 44 | 0.3286 | 0.9070 | 0.9065 | 0.9070 | 0.9041 | | 0.1611 | 5.0 | 55 | 0.3044 | 0.9302 | 0.9307 | 0.9302 | 0.9297 | | 0.1025 | 6.0 | 66 | 0.3227 | 0.9302 | 0.9307 | 0.9302 | 0.9297 | | 0.0799 | 7.0 | 77 | 0.3216 | 0.9302 | 0.9307 | 0.9302 | 0.9297 | | 0.0761 | 8.0 | 88 | 0.3529 | 0.9302 | 0.9307 | 0.9302 | 0.9297 | | 0.0479 | 9.0 | 99 | 0.3605 | 0.9302 | 0.9307 | 0.9302 | 0.9297 | | 0.0358 | 10.0 | 110 | 0.3621 | 0.9302 | 0.9307 | 0.9302 | 0.9297 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Prinernian/distilbert-base-uncased-finetuned-emotion
270938273f7371185bd4d7c28617ccea6d4ca9d7
2022-04-03T09:11:07.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
Prinernian
null
Prinernian/distilbert-base-uncased-finetuned-emotion
5
null
transformers
17,023
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- 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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2208 - Accuracy: 0.924 - F1: 0.9240 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8538 | 1.0 | 250 | 0.3317 | 0.904 | 0.8999 | | 0.2599 | 2.0 | 500 | 0.2208 | 0.924 | 0.9240 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Tokenizers 0.11.6
Zohar/distilgpt2-finetuned-restaurant-reviews-clean
192775c4bf12398beab31035671e61106ae22a4c
2022-04-03T10:29:27.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-generation
false
Zohar
null
Zohar/distilgpt2-finetuned-restaurant-reviews-clean
5
null
transformers
17,024
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-restaurant-reviews-clean results: [] --- <!-- 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. --> # distilgpt2-finetuned-restaurant-reviews-clean This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5371 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7221 | 1.0 | 2447 | 3.5979 | | 3.6413 | 2.0 | 4894 | 3.5505 | | 3.6076 | 3.0 | 7341 | 3.5371 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu102 - Datasets 1.18.2 - Tokenizers 0.11.0
JB173/distilbert-base-uncased-finetuned-emotion
17bd8e4b6269181c9e3fe79059a08d847cdb0b77
2022-04-03T15:27:47.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
JB173
null
JB173/distilbert-base-uncased-finetuned-emotion
5
null
transformers
17,025
Entry not found
LeBenchmark/wav2vec-FR-1K-Female-large
6a969b5a94bbaa0fa951670954e7993f8e7c33e4
2022-05-11T09:22:44.000Z
[ "pytorch", "wav2vec2", "pretraining", "fr", "arxiv:2204.01397", "transformers", "license:apache-2.0" ]
null
false
LeBenchmark
null
LeBenchmark/wav2vec-FR-1K-Female-large
5
null
transformers
17,026
--- language: "fr" thumbnail: tags: - wav2vec2 license: "apache-2.0" --- # LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. For more information about our gender study for SSL moddels, please refer to our paper at: [A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems](https://arxiv.org/abs/2204.01397) ## Model and data descriptions We release four gender-specific models trained on 1K hours of speech. - [wav2vec2-FR-1K-Male-large](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Male-large/) - [wav2vec2-FR-1k-Male-base](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Male-base/) - [wav2vec2-FR-1K-Female-large](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Female-large/) - [wav2vec2-FR-1K-Female-base](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Female-base/) ## Intended uses & limitations Pretrained wav2vec2 models are distributed under the Apache-2.0 license. Hence, they can be reused extensively without strict limitations. However, benchmarks and data may be linked to corpora that are not completely open-sourced. ## Referencing our gender-specific models ``` @article{boito2022study, title={A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems}, author={Marcely Zanon Boito and Laurent Besacier and Natalia Tomashenko and Yannick Est{\`e}ve}, journal={arXiv preprint arXiv:2204.01397}, year={2022} } ``` ## Referencing LeBenchmark ``` @inproceedings{evain2021task, title={Task agnostic and task specific self-supervised learning from speech with \textit{LeBenchmark}}, author={Evain, Sol{\`e}ne and Nguyen, Ha and Le, Hang and Boito, Marcely Zanon and Mdhaffar, Salima and Alisamir, Sina and Tong, Ziyi and Tomashenko, Natalia and Dinarelli, Marco and Parcollet, Titouan and others}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021} } ```
microsoft/cvt-21-384
d73b0f45502a83c5378535ee7ec9b3379de0a8bc
2022-05-18T16:11:18.000Z
[ "pytorch", "cvt", "image-classification", "dataset:imagenet-1k", "arxiv:2103.15808", "transformers", "vision", "license:apache-2.0" ]
image-classification
false
microsoft
null
microsoft/cvt-21-384
5
null
transformers
17,027
--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # Convolutional Vision Transformer (CvT) CvT-21 model pre-trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://github.com/microsoft/CvT). Disclaimer: The team releasing CvT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Usage Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoFeatureExtractor, CvtForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = AutoFeatureExtractor.from_pretrained('microsoft/cvt-21-384') model = CvtForImageClassification.from_pretrained('microsoft/cvt-21-384') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` ```
dapang/distilbert-base-uncased-finetuned-moral-action
f270b3aa0c60aae13ed94f2c5ba0cb30472011fc
2022-04-05T03:21:19.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
dapang
null
dapang/distilbert-base-uncased-finetuned-moral-action
5
null
transformers
17,028
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-moral-action results: [] --- <!-- 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-moral-action This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4632 - Accuracy: 0.7912 - F1: 0.7912 ## 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: 9.716387809233253e-05 - train_batch_size: 2000 - eval_batch_size: 2000 - 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 10 | 0.5406 | 0.742 | 0.7399 | | No log | 2.0 | 20 | 0.4810 | 0.7628 | 0.7616 | | No log | 3.0 | 30 | 0.4649 | 0.786 | 0.7856 | | No log | 4.0 | 40 | 0.4600 | 0.7916 | 0.7916 | | No log | 5.0 | 50 | 0.4632 | 0.7912 | 0.7912 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1 - Datasets 2.0.0 - Tokenizers 0.11.0
dennishe97/longformer-code-mlm
f2e90ff8aea84dac2042aad0e6da1659bbb873f1
2022-04-05T05:45:16.000Z
[ "pytorch", "longformer", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
dennishe97
null
dennishe97/longformer-code-mlm
5
null
transformers
17,029
Entry not found
Seethal/Distilbert-base-uncased-fine-tuned-service-bc
6a16656fa7fc113f0e95c84f757f92235eb26d79
2022-04-05T16:16:47.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
Seethal
null
Seethal/Distilbert-base-uncased-fine-tuned-service-bc
5
null
transformers
17,030
# Sentiment analysis model
Kuray107/ls-timit-100percent-supervised-aug
986164050266757977fdca09c9cf452d8687358a
2022-04-05T20:18:46.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "model-index" ]
automatic-speech-recognition
false
Kuray107
null
Kuray107/ls-timit-100percent-supervised-aug
5
null
transformers
17,031
--- tags: - generated_from_trainer model-index: - name: ls-timit-100percent-supervised-aug results: [] --- <!-- 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. --> # ls-timit-100percent-supervised-aug This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0519 - Wer: 0.0292 ## 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: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2985 | 7.04 | 1000 | 0.0556 | 0.0380 | | 0.1718 | 14.08 | 2000 | 0.0519 | 0.0292 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.2 - Tokenizers 0.10.3
Stremie/roberta-base-clickbait
ddc52807c0bb3f6c524ddb5c59e9e80d098d1372
2022-04-18T12:51:37.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
Stremie
null
Stremie/roberta-base-clickbait
5
null
transformers
17,032
This model classifies whether a tweet is clickbait or not. It has been trained using [Webis-Clickbait-17](https://webis.de/data/webis-clickbait-17.html) dataset. Input is composed of 'postText'. Achieved ~0.7 F1-score on test data.
ICLbioengNLP/CXR_BioClinicalBERT_MLM
7aa68734249bc8e6785caf8095ab7ca86894101b
2022-04-06T19:53:01.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
ICLbioengNLP
null
ICLbioengNLP/CXR_BioClinicalBERT_MLM
5
null
transformers
17,033
Entry not found
Sleoruiz/distilbert-base-uncased-finetuned-cola
142948e2a2c09e34a65732de24437526bd226c84
2022-04-07T13:15:08.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
Sleoruiz
null
Sleoruiz/distilbert-base-uncased-finetuned-cola
5
null
transformers
17,034
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5396261051709696 --- <!-- 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-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7663 - Matthews Correlation: 0.5396 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5281 | 1.0 | 535 | 0.5268 | 0.4071 | | 0.3503 | 2.0 | 1070 | 0.5074 | 0.5126 | | 0.2399 | 3.0 | 1605 | 0.6440 | 0.4977 | | 0.1807 | 4.0 | 2140 | 0.7663 | 0.5396 | | 0.1299 | 5.0 | 2675 | 0.8786 | 0.5192 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
birgermoell/psst-fairseq-common-voice
c9772d2df0d10cc540a60b2dbee7f5dfa1da89c3
2022-04-07T08:30:02.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
birgermoell
null
birgermoell/psst-fairseq-common-voice
5
null
transformers
17,035
Entry not found
AmanPriyanshu/fake-news-detector
19949c17e66e05292f23178662d0c8d3a16390cb
2022-04-07T13:17:05.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
AmanPriyanshu
null
AmanPriyanshu/fake-news-detector
5
null
transformers
17,036
Entry not found
arijitx/IndicBART-bn-QuestionGeneration
0892b45936e46c0d711b119e8d753b61b1fb2ec0
2022-04-07T14:24:09.000Z
[ "pytorch", "mbart", "text2text-generation", "bn", "arxiv:2203.05437", "transformers", "license:mit", "autotrain_compatible" ]
text2text-generation
false
arijitx
null
arijitx/IndicBART-bn-QuestionGeneration
5
null
transformers
17,037
--- license: mit language: - bn tags: - text2text-generation widget: - text: "১৮৯৭ খ্রিষ্টাব্দের ২৩ জানুয়ারি [SEP] সুভাষ ১৮৯৭ খ্রিষ্টাব্দের ২৩ জানুয়ারি ব্রিটিশ ভারতের অন্তর্গত বাংলা প্রদেশের উড়িষ্যা বিভাগের কটকে জন্মগ্রহণ করেন। </s> <2bn>" --- ## Intro Trained on IndicNLGSuit [IndicQuestionGeneration](https://huggingface.co/datasets/ai4bharat/IndicQuestionGeneration) data for Bengali the model is finetuned from [IndicBART](https://huggingface.co/ai4bharat/IndicBART) ## Finetuned Command python run_summarization.py --model_name_or_path bnQG_models/checkpoint-32000 --do_eval --train_file train_bn.json --validation_file valid_bn.json --output_dir bnQG_models --overwrite_output_dir --per_device_train_batch_size=2 --per_device_eval_batch_size=4 --predict_with_generate --text_column src --summary_column tgt --save_steps 4000 --evaluation_strategy steps --gradient_accumulation_steps 4 --eval_steps 1000 --learning_rate 0.001 --num_beams 4 --forced_bos_token "<2bn>" --num_train_epochs 10 --warmup_steps 10000 ## Sample Line from train data {"src": "प्राणबादी [SEP] अर्थाॎ, तिनि छिलेन एकजन सर्बप्राणबादी। </s> <2bn>", "tgt": "<2bn> कोन दार्शनिक दृष्टिभङ्गि ओय़ाइटजेर छिल? </s>"} ## Inference script = "সুভাষ ১৮৯৭ খ্রিষ্টাব্দের ২৩ জানুয়ারি ব্রিটিশ ভারতের অন্তর্গত বাংলা প্রদেশের উড়িষ্যা বিভাগের (অধুনা, ভারতের ওড়িশা রাজ্য) কটকে জন্মগ্রহণ করেন।" answer = "১৮৯৭ খ্রিষ্টাব্দের ২৩ জানুয়ারি" inp = answer +" [SEP] "+script + " </s> <2bn>" inp_tok = tokenizer(inp, add_special_tokens=False, return_tensors="pt", padding=True).input_ids model.eval() # Set dropouts to zero model_output=model.generate(inp_tok, use_cache=True, num_beams=4, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2bn>") ) decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) ## Citations @inproceedings{dabre2021indicbart, title={IndicBART: A Pre-trained Model for Natural Language Generation of Indic Languages}, author={Raj Dabre and Himani Shrotriya and Anoop Kunchukuttan and Ratish Puduppully and Mitesh M. Khapra and Pratyush Kumar}, year={2022}, booktitle={Findings of the Association for Computational Linguistics}, } @misc{kumar2022indicnlg, title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages}, author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar}, year={2022}, eprint={2203.05437}, archivePrefix={arXiv}, primaryClass={cs.CL} }
mdroth/distilbert-base-uncased-finetuned-ner
933d196076eebdd35a9b7d17c6613c778adb95f2
2022-07-13T23:40:24.000Z
[ "pytorch", "tensorboard", "distilbert", "token-classification", "dataset:conll2003", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
mdroth
null
mdroth/distilbert-base-uncased-finetuned-ner
5
null
transformers
17,038
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9299878143347735 - name: Recall type: recall value: 0.9391430808815304 - name: F1 type: f1 value: 0.93454302571524 - name: Accuracy type: accuracy value: 0.9841453921553053 --- <!-- 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-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0635 - Precision: 0.9300 - Recall: 0.9391 - F1: 0.9345 - Accuracy: 0.9841 ## 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: 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0886 | 1.0 | 1756 | 0.0676 | 0.9198 | 0.9233 | 0.9215 | 0.9809 | | 0.0382 | 2.0 | 3512 | 0.0605 | 0.9271 | 0.9360 | 0.9315 | 0.9836 | | 0.0247 | 3.0 | 5268 | 0.0635 | 0.9300 | 0.9391 | 0.9345 | 0.9841 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.9.0 - Datasets 2.0.0 - Tokenizers 0.11.6
btjiong/robbert-twitter-sentiment-custom
658316088e2cf690b86b3b619ee678967c486a56
2022-04-08T08:17:25.000Z
[ "pytorch", "roberta", "text-classification", "dataset:dutch_social", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
btjiong
null
btjiong/robbert-twitter-sentiment-custom
5
null
transformers
17,039
--- license: mit tags: - generated_from_trainer datasets: - dutch_social metrics: - accuracy - f1 - precision - recall model-index: - name: robbert-twitter-sentiment-custom results: - task: name: Text Classification type: text-classification dataset: name: dutch_social type: dutch_social args: dutch_social metrics: - name: Accuracy type: accuracy value: 0.788 - name: F1 type: f1 value: 0.7878005279207152 - name: Precision type: precision value: 0.7877102066609215 - name: Recall type: recall value: 0.788 --- <!-- 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. --> # robbert-twitter-sentiment-custom This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset. It achieves the following results on the evaluation set: - Loss: 0.6656 - Accuracy: 0.788 - F1: 0.7878 - Precision: 0.7877 - Recall: 0.788 ## 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: 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 | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.8287 | 1.0 | 282 | 0.7178 | 0.7007 | 0.6958 | 0.6973 | 0.7007 | | 0.4339 | 2.0 | 564 | 0.5873 | 0.7667 | 0.7668 | 0.7681 | 0.7667 | | 0.2045 | 3.0 | 846 | 0.6656 | 0.788 | 0.7878 | 0.7877 | 0.788 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cpu - Datasets 2.0.0 - Tokenizers 0.11.6
Cheltone/BERT_Base_Finetuned_C19Vax
66059e96dc03dda609da7fd5cec5ce86019e252c
2022-04-08T10:55:42.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
Cheltone
null
Cheltone/BERT_Base_Finetuned_C19Vax
5
null
transformers
17,040
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - accuracy - f1 model-index: - name: Bert_Test results: [] --- <!-- 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. --> # Bert_Test This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1965 - Precision: 0.9332 - Accuracy: 0.9223 - F1: 0.9223 ## 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|:------:| | 0.6717 | 0.4 | 500 | 0.6049 | 0.7711 | 0.6743 | 0.6112 | | 0.5704 | 0.8 | 1000 | 0.5299 | 0.7664 | 0.7187 | 0.6964 | | 0.52 | 1.2 | 1500 | 0.4866 | 0.7698 | 0.7537 | 0.7503 | | 0.4792 | 1.6 | 2000 | 0.4292 | 0.8031 | 0.793 | 0.7927 | | 0.4332 | 2.0 | 2500 | 0.3920 | 0.8318 | 0.8203 | 0.8198 | | 0.381 | 2.4 | 3000 | 0.3723 | 0.9023 | 0.8267 | 0.8113 | | 0.3625 | 2.8 | 3500 | 0.3134 | 0.8736 | 0.8607 | 0.8601 | | 0.3325 | 3.2 | 4000 | 0.2924 | 0.8973 | 0.871 | 0.8683 | | 0.3069 | 3.6 | 4500 | 0.2671 | 0.8916 | 0.8847 | 0.8851 | | 0.2866 | 4.0 | 5000 | 0.2571 | 0.8920 | 0.8913 | 0.8926 | | 0.2595 | 4.4 | 5500 | 0.2450 | 0.8980 | 0.9 | 0.9015 | | 0.2567 | 4.8 | 6000 | 0.2246 | 0.9057 | 0.9043 | 0.9054 | | 0.2255 | 5.2 | 6500 | 0.2263 | 0.9332 | 0.905 | 0.9030 | | 0.2237 | 5.6 | 7000 | 0.2083 | 0.9265 | 0.9157 | 0.9156 | | 0.2248 | 6.0 | 7500 | 0.2039 | 0.9387 | 0.9193 | 0.9185 | | 0.2086 | 6.4 | 8000 | 0.2038 | 0.9436 | 0.9193 | 0.9181 | | 0.2029 | 6.8 | 8500 | 0.1965 | 0.9332 | 0.9223 | 0.9223 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Splend1dchan/canine-c-squad
3106343c2dfdea56198087ed9ff582a60e344892
2022-04-08T14:42:24.000Z
[ "pytorch", "canine", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
Splend1dchan
null
Splend1dchan/canine-c-squad
5
null
transformers
17,041
python run_squad.py \ --model_name_or_path google/canine-c \ --do_train \ --do_eval \ --per_gpu_train_batch_size 1 \ --per_gpu_eval_batch_size 1 \ --gradient_accumulation_steps 128 \ --learning_rate 3e-5 \ --num_train_epochs 3 \ --max_seq_length 1024 \ --doc_stride 128 \ --max_answer_length 240 \ --output_dir canine-c-squad \ --model_type bert { "_name_or_path": "google/canine-c", "architectures": [ "CanineForQuestionAnswering" ], "attention_probs_dropout_prob": 0.1, "bos_token_id": 57344, "downsampling_rate": 4, "eos_token_id": 57345, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-12, "local_transformer_stride": 128, "max_position_embeddings": 16384, "model_type": "canine", "num_attention_heads": 12, "num_hash_buckets": 16384, "num_hash_functions": 8, "num_hidden_layers": 12, "pad_token_id": 0, "torch_dtype": "float32", "transformers_version": "4.19.0.dev0", "type_vocab_size": 16, "upsampling_kernel_size": 4, "use_cache": true } {'exact': 58.893093661305585, 'f1': 72.18823344945899}
Eugen/distilbert-base-uncased-finetuned-stsb
89fb267f4ab52276278c66ef0a6b4f1b4938fd27
2022-04-08T20:00:06.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
Eugen
null
Eugen/distilbert-base-uncased-finetuned-stsb
5
null
transformers
17,042
Entry not found
caush/TestMeanFraction2
953cbc5ee6c1024ab8d9e8b0550b607f6d2022e9
2022-04-08T17:51:14.000Z
[ "pytorch", "tensorboard", "camembert", "text-classification", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
caush
null
caush/TestMeanFraction2
5
null
transformers
17,043
--- license: mit tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: TestMeanFraction2 results: [] --- <!-- 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. --> # TestMeanFraction2 This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3967 - Matthews Correlation: 0.2537 ## Model description More information needed ## Intended uses & limitations "La panique totale" Cette femme trouve une énorme araignée suspendue à sa douche. ## 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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 0.13 | 50 | 1.1126 | 0.1589 | | No log | 0.25 | 100 | 1.0540 | 0.1884 | | No log | 0.38 | 150 | 1.1533 | 0.0818 | | No log | 0.51 | 200 | 1.0676 | 0.1586 | | No log | 0.64 | 250 | 0.9949 | 0.2280 | | No log | 0.76 | 300 | 1.0343 | 0.2629 | | No log | 0.89 | 350 | 1.0203 | 0.2478 | | No log | 1.02 | 400 | 1.0041 | 0.2752 | | No log | 1.15 | 450 | 1.0808 | 0.2256 | | 1.023 | 1.27 | 500 | 1.0029 | 0.2532 | | 1.023 | 1.4 | 550 | 1.0204 | 0.2508 | | 1.023 | 1.53 | 600 | 1.1377 | 0.1689 | | 1.023 | 1.65 | 650 | 1.0499 | 0.2926 | | 1.023 | 1.78 | 700 | 1.0441 | 0.2474 | | 1.023 | 1.91 | 750 | 1.0279 | 0.2611 | | 1.023 | 2.04 | 800 | 1.1511 | 0.2804 | | 1.023 | 2.16 | 850 | 1.2381 | 0.2512 | | 1.023 | 2.29 | 900 | 1.3340 | 0.2385 | | 1.023 | 2.42 | 950 | 1.4372 | 0.2842 | | 0.7325 | 2.54 | 1000 | 1.3967 | 0.2537 | | 0.7325 | 2.67 | 1050 | 1.4272 | 0.2624 | | 0.7325 | 2.8 | 1100 | 1.3869 | 0.1941 | | 0.7325 | 2.93 | 1150 | 1.4983 | 0.2063 | | 0.7325 | 3.05 | 1200 | 1.4959 | 0.2409 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0a0+0aef44c - Datasets 2.0.0 - Tokenizers 0.11.6
mcclane/movie-director-predictor
7eacf983e7878ad9a1ae0d63aadc620c9e78b94e
2022-04-08T20:41:49.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
mcclane
null
mcclane/movie-director-predictor
5
null
transformers
17,044
Entry not found
malcolm/TSC_finetuning-sentiment-movie-model2
57d8ebfe6711e670f06bbd476737d8d692c9723d
2022-04-09T03:26:26.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
malcolm
null
malcolm/TSC_finetuning-sentiment-movie-model2
5
null
transformers
17,045
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: TSC_finetuning-sentiment-movie-model2 results: [] --- <!-- 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. --> # TSC_finetuning-sentiment-movie-model2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1479 - Accuracy: 0.957 - F1: 0.9752 ## 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: 2 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Cheltone/DistilRoBERTa-C19-Vax-Fine-tuned
a11d6ad460066b74a84254e9b968a723c640009a
2022-04-12T00:34:14.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
Cheltone
null
Cheltone/DistilRoBERTa-C19-Vax-Fine-tuned
5
null
transformers
17,046
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - accuracy - f1 model-index: - name: DistilRoberta results: [] --- <!-- 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. --> # DistilRoberta This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1246 - Precision: 0.9633 - Accuracy: 0.9697 - F1: 0.9705 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|:------:| | 0.5894 | 0.4 | 500 | 0.4710 | 0.8381 | 0.7747 | 0.7584 | | 0.3863 | 0.8 | 1000 | 0.3000 | 0.8226 | 0.8737 | 0.8858 | | 0.2272 | 1.2 | 1500 | 0.1973 | 0.9593 | 0.9333 | 0.9329 | | 0.1639 | 1.6 | 2000 | 0.1694 | 0.9067 | 0.9367 | 0.9403 | | 0.1263 | 2.0 | 2500 | 0.1128 | 0.9657 | 0.9597 | 0.9603 | | 0.0753 | 2.4 | 3000 | 0.1305 | 0.9614 | 0.967 | 0.9679 | | 0.0619 | 2.8 | 3500 | 0.1246 | 0.9633 | 0.9697 | 0.9705 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
cathen/test_model_car
1bf8f5bb42463f1a63cfb906257c628be212b3b6
2022-04-10T22:06:35.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
cathen
null
cathen/test_model_car
5
null
transformers
17,047
Entry not found
baikal/electra-wp30
cbae3154f40344932954bd7277ffdc6ddb0827f9
2022-04-11T03:48:41.000Z
[ "pytorch", "electra", "pretraining", "ko", "dataset:한국어위키", "dataset:국립국어원 문어데이터셋", "transformers" ]
null
false
baikal
null
baikal/electra-wp30
5
null
transformers
17,048
--- language: ko datasets: - 한국어위키 - 국립국어원 문어데이터셋 --- ELECTRA-base --- - model: electra-base-discriminator - vocab: bert-wordpiece, 30,000
vocab-transformers/distilbert-word2vec_256k-MLM_best
ee249caa94fb88a16954a85137efc67000c424a1
2022-04-11T11:13:13.000Z
[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
vocab-transformers
null
vocab-transformers/distilbert-word2vec_256k-MLM_best
5
null
transformers
17,049
# DistilBERT with word2vec token embeddings This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs. Then the model was trained on this dataset with MLM for 1.37M steps (batch size 64). The token embeddings were NOT updated. For the initial word2vec weights with Gensim see: [https://huggingface.co/vocab-transformers/distilbert-word2vec_256k-MLM_1M/tree/main/word2vec](https://huggingface.co/vocab-transformers/distilbert-word2vec_256k-MLM_1M/tree/main/word2vec)
maveriq/lingbert-base-32k
bd816fcc8f80936a2c7b49d14cdaf595ed43ece3
2022-04-11T17:16:51.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
maveriq
null
maveriq/lingbert-base-32k
5
null
transformers
17,050
Entry not found
adache/distilbert-base-uncased-finetuned-emotion
368fca48c897a445f86c2786ea25f704c56d15d7
2022-04-12T07:48:16.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
adache
null
adache/distilbert-base-uncased-finetuned-emotion
5
null
transformers
17,051
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- 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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2270 - Accuracy: 0.9245 - F1: 0.9249 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8398 | 1.0 | 250 | 0.3276 | 0.9005 | 0.8966 | | 0.2541 | 2.0 | 500 | 0.2270 | 0.9245 | 0.9249 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Tokenizers 0.11.6
philschmid/tiny-random-wav2vec2
5c8f88769434b22d60a6c6cc848e56677141eef7
2022-04-12T06:14:01.000Z
[ "pytorch", "tf", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
philschmid
null
philschmid/tiny-random-wav2vec2
5
null
transformers
17,052
Entry not found
Splend1dchan/wav2vec2-large-10min-lv60-self
287652e731abad05cd3c57b9d10dce15aedc18d4
2022-05-30T04:37:27.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:librispeech_asr", "arxiv:2010.11430", "arxiv:2006.11477", "transformers", "speech", "audio", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Splend1dchan
null
Splend1dchan/wav2vec2-large-10min-lv60-self
5
null
transformers
17,053
--- language: en datasets: - librispeech_asr tags: - speech - audio - automatic-speech-recognition - hf-asr-leaderboard license: apache-2.0 model-index: - name: wav2vec2-large-10min-lv60 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Librispeech (clean) type: librispeech_asr args: en metrics: - name: Test WER type: wer value: None --- # Wav2Vec2-Large-10min-Lv60 + Self-Training # This is a direct state_dict transfer from fairseq to huggingface, the weights are identical [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The large model pretrained and fine-tuned on 10min of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with [Self-Training objective](https://arxiv.org/abs/2010.11430). When using the model make sure that your speech input is also sampled at 16Khz. [Paper](https://arxiv.org/abs/2006.11477) Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli **Abstract** They show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("Splend1dchan/wav2vec2-large-10min-lv60-self") model = Wav2Vec2ForCTC.from_pretrained("Splend1dchan/wav2vec2-large-10min-lv60-self") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ``` ## Evaluation This code snippet shows how to evaluate facebook's **Splend1dchan/wav2vec2-large-10min-lv60-self** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch from jiwer import wer librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") model = Wav2Vec2ForCTC.from_pretrained("Splend1dchan/wav2vec2-large-10min-lv60-self").to("cuda") processor = Wav2Vec2Processor.from_pretrained("Splend1dchan/wav2vec2-large-10min-lv60-self") def map_to_pred(batch): inputs = processor(batch["audio"]["array"], return_tensors="pt", padding="longest") input_values = inputs.input_values.to("cuda") attention_mask = inputs.attention_mask.to("cuda") with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = librispeech_eval.map(map_to_pred, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"])) ``` <!-- *Result (WER)*: | "clean" | "other" | |---|---| | untested | untested | -->
conviette/korPolBERT
75737219014da62a0fc94f43ddc61f526d4ba6b7
2022-04-25T04:00:29.000Z
[ "pytorch", "bert", "text-classification", "transformers", "license:apache-2.0" ]
text-classification
false
conviette
null
conviette/korPolBERT
5
null
transformers
17,054
--- license: apache-2.0 --- This model is a binary classifier developed to analyze comment authorship patterns on Korean news articles. For further details, refer to our paper on Journalism: [News comment sections and online echo chambers: The ideological alignment between partisan news stories and their user comments](https://journals.sagepub.com/doi/full/10.1177/14648849211069241) * This model is a BERT classification model to classify Korean user generated comments into binary labels of liberal or conservative. * This model was trained on approximately 37,000 user generated comments collected from NAVER\'s news portal. The dataset was collected in 2019; as such, note that comments related to recent political topics might not be classified correctly. * This model is a finetuned model based on ETRI\'s KorBERT. ### How to use * The model requires an edited version of the transformers class `BertTokenizer`, which can be found in the file `KorBertTokenizer.py`. * Usage example: ~~~python from KorBertTokenizer import KorBertTokenizer from transformers import BertForSequenceClassification import torch tokenizer = KorBertTokenizer.from_pretrained('conviette/korPolBERT') model = BertForSequenceClassification.from_pretrained('conviette/korPolBERT') def classify(text): inputs = tokenizer(text, padding='max_length', max_length=70, return_tensors='pt') with torch.no_grad(): logits=model(**inputs).logits predicted_class_id = logits.argmax().item() return model.config.id2label[predicted_class_id] input_strings = ['좌파가 나라 경제 안보 말아먹는다', '수꼴들은 나라 일본한테 팔아먹었냐'] for input_string in input_strings: print('===\n입력 텍스트: {}\n분류 결과: {}\n==='.format(input_string, classify(input_string))) ~~~ ### Model performance * Accuracy: 0.8322 * F1-Score: 0.8322 * For further technical details on the model, refer to our paper for the W-NUT workshop (EMNLP 2019), [The Fallacy of Echo Chambers: Analyzing the Political Slants of User-Generated News Comments in Korean Media](https://aclanthology.org/D19-5548/).
Jatin-WIAI/tamil_relevance_clf
9138ffeb31e6b62e459659d295031015df4dcfbe
2022-04-12T10:05:33.000Z
[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]
text-classification
false
Jatin-WIAI
null
Jatin-WIAI/tamil_relevance_clf
5
null
transformers
17,055
Entry not found
CenIA/bert-base-spanish-wwm-uncased-finetuned-qa-sqac
19c252ec0fb39e6fa584b225fc0b25cc5242aac0
2022-04-13T14:42:37.000Z
[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
CenIA
null
CenIA/bert-base-spanish-wwm-uncased-finetuned-qa-sqac
5
null
transformers
17,056
Entry not found
Xuan-Rui/pet-10-all
51e389a4c414b49ff55a02474a91677a9d0acdc3
2022-04-13T05:46:48.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
Xuan-Rui
null
Xuan-Rui/pet-10-all
5
null
transformers
17,057
Entry not found
Xuan-Rui/pet-1000-p4
bcc1032294fa85e32f75d8b4ec5f28198a4001be
2022-04-13T07:00:21.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
Xuan-Rui
null
Xuan-Rui/pet-1000-p4
5
null
transformers
17,058
Entry not found
Xuan-Rui/pet-1000-all
43dc21f9c03cba614d400496e1d2d8059a4d66d7
2022-04-13T07:06:32.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
Xuan-Rui
null
Xuan-Rui/pet-1000-all
5
null
transformers
17,059
Entry not found
raquiba/distilbert-base-uncased-finetuned-ner
53ff8704380e4fb11bc807d2345acb507aeb4e34
2022-04-14T11:42:48.000Z
[ "pytorch", "tensorboard", "distilbert", "token-classification", "dataset:conll2003", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
raquiba
null
raquiba/distilbert-base-uncased-finetuned-ner
5
null
transformers
17,060
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9213829552961903 - name: Recall type: recall value: 0.9361226087929299 - name: F1 type: f1 value: 0.9286943010931691 - name: Accuracy type: accuracy value: 0.9831604365577391 --- <!-- 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-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0619 - Precision: 0.9214 - Recall: 0.9361 - F1: 0.9287 - Accuracy: 0.9832 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2399 | 1.0 | 878 | 0.0738 | 0.9084 | 0.9178 | 0.9131 | 0.9793 | | 0.0555 | 2.0 | 1756 | 0.0610 | 0.9207 | 0.9340 | 0.9273 | 0.9825 | | 0.0305 | 3.0 | 2634 | 0.0619 | 0.9214 | 0.9361 | 0.9287 | 0.9832 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.12.1
vabadeh213/autotrain-iine_classification10-737422470
285f9e2f30a4fc68b3cebef1c7d05c985b079522
2022-04-13T09:24:04.000Z
[ "pytorch", "bert", "text-classification", "ja", "dataset:vabadeh213/autotrain-data-iine_classification10", "transformers", "autotrain", "co2_eq_emissions" ]
text-classification
false
vabadeh213
null
vabadeh213/autotrain-iine_classification10-737422470
5
null
transformers
17,061
--- tags: autotrain language: ja widget: - text: "RustでWebAssemblyインタプリタを作った話+webassembly+rust" - text: "Goのロギングライブラリ 2021年冬 golang library logging go" - text: "VimとTUIツールをなめらかに切り替える ranger tig git vim" datasets: - vabadeh213/autotrain-data-iine_classification10 co2_eq_emissions: 7.351885824089346 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 737422470 - CO2 Emissions (in grams): 7.351885824089346 ## Validation Metrics - Loss: 0.39456263184547424 - Accuracy: 0.8279088689991864 - Precision: 0.6869806094182825 - Recall: 0.17663817663817663 - AUC: 0.7937892215111646 - F1: 0.2810198300283286 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/vabadeh213/autotrain-iine_classification10-737422470 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("vabadeh213/autotrain-iine_classification10-737422470", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("vabadeh213/autotrain-iine_classification10-737422470", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
cometrain/fake-news-detector-t5
6b80e1081e1f444f24f4293d58503ec67c5c5244
2022-04-13T11:57:12.000Z
[ "pytorch", "t5", "text2text-generation", "en", "dataset:fake-and-real-news-dataset", "transformers", "Cometrain AutoCode", "Cometrain AlphaML", "autotrain_compatible" ]
text2text-generation
false
cometrain
null
cometrain/fake-news-detector-t5
5
null
transformers
17,062
--- language: - en tags: - Cometrain AutoCode - Cometrain AlphaML datasets: - fake-and-real-news-dataset widget: - text: "Former FBI Agent: We've never been to the moon" example_title: "Apollo program misinformation" - text: "Finland to make decision on NATO membership in coming weeks" example_title: "Article from Reuters about Finland & NATO" inference: parameters: top_p: 0.9 temperature: 0.5 --- # fake-news-detector-t5 This model has been automatically fine-tuned and tested as part of the development of the GPT-2-based AutoML framework for accelerated and easy development of NLP enterprise solutions. Fine-tuned [T5](https://huggingface.co/t5-base) allows to recognize fake news and misinformation. Automatically trained on [Fake and real news dataset(2017)](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) dataset. ## Made with Cometrain AlphaML & AutoCode This model was automatically fine-tuned using the Cometrain AlphaML framework and tested with CI/CD pipeline made by Cometrain AutoCode ## Cometrain AlphaML command ```shell $ cometrain create --name fake-news-detector --model auto --task 'Finetune the machine learning model for recognizing fake news' --output transformers ```
Helsinki-NLP/opus-mt-tc-big-en-lt
60f0ff0262c5f85d1b924328e123cb8ae1e5590a
2022-06-01T13:03:38.000Z
[ "pytorch", "marian", "text2text-generation", "en", "lt", "transformers", "translation", "opus-mt-tc", "license:cc-by-4.0", "model-index", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-tc-big-en-lt
5
null
transformers
17,063
--- language: - en - lt tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-en-lt results: - task: name: Translation eng-lit type: translation args: eng-lit dataset: name: flores101-devtest type: flores_101 args: eng lit devtest metrics: - name: BLEU type: bleu value: 28.0 - task: name: Translation eng-lit type: translation args: eng-lit dataset: name: newsdev2019 type: newsdev2019 args: eng-lit metrics: - name: BLEU type: bleu value: 26.6 - task: name: Translation eng-lit type: translation args: eng-lit dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-lit metrics: - name: BLEU type: bleu value: 39.5 - task: name: Translation eng-lit type: translation args: eng-lit dataset: name: newstest2019 type: wmt-2019-news args: eng-lit metrics: - name: BLEU type: bleu value: 17.5 --- # opus-mt-tc-big-en-lt Neural machine translation model for translating from English (en) to Lithuanian (lt). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-02-25 * source language(s): eng * target language(s): lit * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-02-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lit/opusTCv20210807+bt_transformer-big_2022-02-25.zip) * more information released models: [OPUS-MT eng-lit README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-lit/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "A cat was sitting on the chair.", "Yukiko likes potatoes." ] model_name = "pytorch-models/opus-mt-tc-big-en-lt" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Katė sėdėjo ant kėdės. # Jukiko mėgsta bulves. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-lt") print(pipe("A cat was sitting on the chair.")) # expected output: Katė sėdėjo ant kėdės. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-02-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lit/opusTCv20210807+bt_transformer-big_2022-02-25.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lit/opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | eng-lit | tatoeba-test-v2021-08-07 | 0.67434 | 39.5 | 2528 | 14942 | | eng-lit | flores101-devtest | 0.59593 | 28.0 | 1012 | 20695 | | eng-lit | newsdev2019 | 0.58444 | 26.6 | 2000 | 39627 | | eng-lit | newstest2019 | 0.51559 | 17.5 | 998 | 19711 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 3405783 * port time: Wed Apr 13 17:42:39 EEST 2022 * port machine: LM0-400-22516.local
dbounds/roberta-large-finetuned-clinc
9ac07f17ca93990c893a1603bf2fab16ff812375
2022-04-13T16:30:14.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
dbounds
null
dbounds/roberta-large-finetuned-clinc
5
null
transformers
17,064
--- license: mit tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: roberta-large-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9741935483870968 --- <!-- 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-large-finetuned-clinc This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.1594 - Accuracy: 0.9742 ## 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 - distributed_type: sagemaker_data_parallel - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 5.0651 | 1.0 | 120 | 5.0213 | 0.0065 | | 4.2482 | 2.0 | 240 | 2.5682 | 0.7997 | | 1.694 | 3.0 | 360 | 0.6019 | 0.9445 | | 0.4594 | 4.0 | 480 | 0.2330 | 0.9655 | | 0.1599 | 5.0 | 600 | 0.1594 | 0.9742 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6
ASCCCCCCCC/PENGMENGJIE-finetuned-sms
5b22afa690dd3f81adf4e269eb90882ab65c3f23
2022-04-14T07:57:02.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "model-index" ]
text-classification
false
ASCCCCCCCC
null
ASCCCCCCCC/PENGMENGJIE-finetuned-sms
5
null
transformers
17,065
--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: PENGMENGJIE-finetuned-sms results: [] --- <!-- 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. --> # PENGMENGJIE-finetuned-sms This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 - F1: 1.0 ## 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: 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0116 | 1.0 | 1250 | 0.0060 | 0.999 | 0.9990 | | 0.003 | 2.0 | 2500 | 0.0000 | 1.0 | 1.0 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6
Srini99/TQA
e937ad55866ba1d7246c04239018850cee70a16d
2022-04-14T13:15:13.000Z
[ "pytorch", "xlm-roberta", "question-answering", "multilingual", "Tamil", "dataset:squad v2", "dataset:chaii", "dataset:mlqa", "dataset:xquad", "transformers", "autotrain_compatible" ]
question-answering
false
Srini99
null
Srini99/TQA
5
null
transformers
17,066
--- language: - multilingual - Tamil tags: - question-answering datasets: - squad v2 - chaii - mlqa - xquad metrics: - Exact Match - F1 widget: - text: "சென்னையில் எத்தனை மக்கள் வாழ்கின்றனர்?" context: "சென்னை (Chennai) தமிழ்நாட்டின் தலைநகரமும் இந்தியாவின் நான்காவது பெரிய நகரமும் ஆகும். 1996 ஆம் ஆண்டுக்கு முன்னர் இந்நகரம் மெட்ராஸ் (Madras) என்று அழைக்கப்பட்டு வந்தது. சென்னை, வங்காள விரிகுடாவின் கரையில் அமைந்த துறைமுக நகரங்களுள் ஒன்று. சுமார் 10 மில்லியன் (ஒரு கோடி) மக்கள் வாழும் இந்நகரம், உலகின் 35 பெரிய மாநகரங்களுள் ஒன்று. 17ஆம் நூற்றாண்டில் ஆங்கிலேயர் சென்னையில் கால் பதித்தது முதல், சென்னை நகரம் ஒரு முக்கிய நகரமாக வளர்ந்து வந்திருக்கிறது. சென்னை தென்னிந்தியாவின் வாசலாகக் கருதப்படுகிறது. சென்னை நகரில் உள்ள மெரினா கடற்கரை உலகின் நீளமான கடற்கரைகளுள் ஒன்று. சென்னை கோலிவுட் (Kollywood) என அறியப்படும் தமிழ்த் திரைப்படத் துறையின் தாயகம். பல விளையாட்டு அரங்கங்கள் உள்ள சென்னையில் பல விளையாட்டுப் போட்டிகளும் நடைபெறுகின்றன." example_title: "Question Answering" --- # XLM-RoBERTa Large trained on Dravidian Language QA ## Overview **Language model:** XLM-RoBERTa-lg **Language:** Multilingual, focussed on Tamil & Hindi **Downstream-task:** Extractive QA **Eval data:** K-Fold on Training Data ## Hyperparameters ``` batch_size = 4 base_LM_model = "xlm-roberta-large" learning_rate = 1e-5 optimizer = AdamW weight_decay = 1e-2 epsilon = 1e-8 max_grad_norm = 1.0 lr_schedule = LinearWarmup warmup_proportion = 0.2 max_seq_len = 256 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on our human annotated dataset with 1000 tamil question-context pairs [link] ``` "em": 77.536, "f1": 85.665 ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "Srini99/FYP_TamilQA" model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'யாரால் பொங்கல் சிறப்பாகக் கொண்டாடப்படுகிறது?', 'context': 'பொங்கல் என்பது தமிழர்களால் சிறப்பாகக் கொண்டாடப்படும் ஓர் அறுவடைப் பண்டிகை ஆகும்.' } res = nlp(QA_input) ```
achyut/patronizing_detection
0103f058fe766023975e00a01310eb32ef37ded9
2022-04-21T05:18:01.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
achyut
null
achyut/patronizing_detection
5
0
transformers
17,067
This model is fine tuned for Patronizing and Condescending Language Classification task. Have fun.
brad1141/oldData_BERT
715d5c134c0780bb1d359cdb59d3fa7b4a8d7fb9
2022-04-14T21:27:01.000Z
[ "pytorch", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
brad1141
null
brad1141/oldData_BERT
5
null
transformers
17,068
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: oldData_BERT results: [] --- <!-- 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. --> # oldData_BERT This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0616 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2348 | 1.0 | 1125 | 1.0185 | | 1.0082 | 2.0 | 2250 | 0.7174 | | 0.699 | 3.0 | 3375 | 0.3657 | | 0.45 | 4.0 | 4500 | 0.1880 | | 0.2915 | 5.0 | 5625 | 0.1140 | | 0.2056 | 6.0 | 6750 | 0.0708 | | 0.1312 | 7.0 | 7875 | 0.0616 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
agdsga/chinese-bert-wwm-finetuned-product-1
97997ec5b14c5525208f5c4af79cdb5ed76e4285
2022-04-15T06:06:27.000Z
[ "pytorch", "tensorboard", "bert", "text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-generation
false
agdsga
null
agdsga/chinese-bert-wwm-finetuned-product-1
5
null
transformers
17,069
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: chinese-bert-wwm-finetuned-product-1 results: [] --- <!-- 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. --> # chinese-bert-wwm-finetuned-product-1 This model is a fine-tuned version of [hfl/chinese-bert-wwm](https://huggingface.co/hfl/chinese-bert-wwm) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0000 - eval_runtime: 10.6737 - eval_samples_per_second: 362.572 - eval_steps_per_second: 5.715 - epoch: 11.61 - step: 18797 ## 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: 256 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Framework versions - Transformers 4.17.0 - Pytorch 1.6.0 - Datasets 2.0.0 - Tokenizers 0.11.6
malcolm/REA_GenderIdentification_v1
49ddc67ea8eae13a4c404d2d5493899122954fc9
2022-04-15T08:38:29.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
malcolm
null
malcolm/REA_GenderIdentification_v1
5
null
transformers
17,070
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: REA_GenderIdentification_v1 results: [] --- <!-- 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. --> # REA_GenderIdentification_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3366 - Accuracy: 0.8798 - F1: 0.8522 ## 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: 2 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
MartinoMensio/racism-models-m-vote-strict-epoch-3
2743b83c880f0ab25c1090ad95db14413ba114c5
2022-05-04T16:09:42.000Z
[ "pytorch", "bert", "text-classification", "es", "transformers", "license:mit" ]
text-classification
false
MartinoMensio
null
MartinoMensio/racism-models-m-vote-strict-epoch-3
5
null
transformers
17,071
--- language: es license: mit widget: - text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!" --- ### Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022) We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models: | method | epoch 1 | epoch 3 | epoch 3 | epoch 4 | |--- |--- |--- |--- |--- | | raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) | | m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) | | m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) | | regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) | | w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) | | w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) | This model is `m-vote-strict-epoch-3` ### Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name = 'm-vote-strict-epoch-3' tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") full_model_path = f'MartinoMensio/racism-models-{model_name}' model = AutoModelForSequenceClassification.from_pretrained(full_model_path) pipe = pipeline("text-classification", model = model, tokenizer = tokenizer) texts = [ 'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!', 'Es que los judíos controlan el mundo' ] print(pipe(texts)) # [{'label': 'racist', 'score': 0.9929012656211853}, {'label': 'non-racist', 'score': 0.5616322159767151}] ``` For more details, see https://github.com/preyero/neatclass22
MartinoMensio/racism-models-w-m-vote-strict-epoch-2
5fa46b2b78381972e4dbc23fa3cf863cd648e457
2022-05-04T16:25:07.000Z
[ "pytorch", "bert", "text-classification", "es", "transformers", "license:mit" ]
text-classification
false
MartinoMensio
null
MartinoMensio/racism-models-w-m-vote-strict-epoch-2
5
null
transformers
17,072
--- language: es license: mit widget: - text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!" --- ### Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022) We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models: | method | epoch 1 | epoch 3 | epoch 3 | epoch 4 | |--- |--- |--- |--- |--- | | raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) | | m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) | | m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) | | regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) | | w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) | | w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) | This model is `w-m-vote-strict-epoch-2` ### Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name = 'w-m-vote-strict-epoch-2' tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") full_model_path = f'MartinoMensio/racism-models-{model_name}' model = AutoModelForSequenceClassification.from_pretrained(full_model_path) pipe = pipeline("text-classification", model = model, tokenizer = tokenizer) texts = [ 'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!', 'Es que los judíos controlan el mundo' ] print(pipe(texts)) # [{'label': 'racist', 'score': 0.8647435903549194}, {'label': 'non-racist', 'score': 0.9660486578941345}] ``` For more details, see https://github.com/preyero/neatclass22
MartinoMensio/racism-models-w-m-vote-strict-epoch-3
cb7b4608fd92c4fc1d0ee0e2808dc62e292be938
2022-05-04T16:26:07.000Z
[ "pytorch", "bert", "text-classification", "es", "transformers", "license:mit" ]
text-classification
false
MartinoMensio
null
MartinoMensio/racism-models-w-m-vote-strict-epoch-3
5
null
transformers
17,073
--- language: es license: mit widget: - text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!" --- ### Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022) We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models: | method | epoch 1 | epoch 3 | epoch 3 | epoch 4 | |--- |--- |--- |--- |--- | | raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) | | m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) | | m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) | | regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) | | w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) | | w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) | This model is `w-m-vote-strict-epoch-3` ### Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name = 'w-m-vote-strict-epoch-3' tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") full_model_path = f'MartinoMensio/racism-models-{model_name}' model = AutoModelForSequenceClassification.from_pretrained(full_model_path) pipe = pipeline("text-classification", model = model, tokenizer = tokenizer) texts = [ 'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!', 'Es que los judíos controlan el mundo' ] print(pipe(texts)) # [{'label': 'racist', 'score': 0.9619585871696472}, {'label': 'non-racist', 'score': 0.9396700859069824}] ``` For more details, see https://github.com/preyero/neatclass22
MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2
6d37dde0ff7297036ab038e10a15c94c6670fc3f
2022-05-04T16:28:04.000Z
[ "pytorch", "bert", "text-classification", "es", "transformers", "license:mit" ]
text-classification
false
MartinoMensio
null
MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2
5
null
transformers
17,074
--- language: es license: mit widget: - text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!" --- ### Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022) We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models: | method | epoch 1 | epoch 3 | epoch 3 | epoch 4 | |--- |--- |--- |--- |--- | | raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) | | m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) | | m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) | | regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) | | w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) | | w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) | This model is `w-m-vote-nonstrict-epoch-2` ### Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name = 'w-m-vote-nonstrict-epoch-2' tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") full_model_path = f'MartinoMensio/racism-models-{model_name}' model = AutoModelForSequenceClassification.from_pretrained(full_model_path) pipe = pipeline("text-classification", model = model, tokenizer = tokenizer) texts = [ 'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!', 'Es que los judíos controlan el mundo' ] print(pipe(texts)) # [{'label': 'racist', 'score': 0.9680026173591614}, {'label': 'non-racist', 'score': 0.9936750531196594}] ``` For more details, see https://github.com/preyero/neatclass22
aseifert/comma-xlm-roberta-base
0fda25343a38aab69e6c87c0eb1cba45649b7455
2022-04-15T21:08:32.000Z
[ "pytorch", "xlm-roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
aseifert
null
aseifert/comma-xlm-roberta-base
5
null
transformers
17,075
Entry not found
jason9693/klue-roberta-small-apeach
e9436f2c8c0cd0348c8b5d067503faf9dca09c2f
2022-04-16T14:21:11.000Z
[ "pytorch", "roberta", "text-classification", "ko", "dataset:jason9693/APEACH", "transformers" ]
text-classification
false
jason9693
null
jason9693/klue-roberta-small-apeach
5
null
transformers
17,076
--- language: ko widget: - text: "응 어쩔티비~~" datasets: - jason9693/APEACH ---
Raychanan/bert-bert-cased-first512-Conflict-SEP
2d880a94115152ff96da443e7d340e7a47f1298f
2022-04-16T19:16:41.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
Raychanan
null
Raychanan/bert-bert-cased-first512-Conflict-SEP
5
null
transformers
17,077
--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 - accuracy - precision - recall model-index: - name: bert-bert-cased-first512-Conflict-SEP results: [] --- <!-- 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. --> # bert-bert-cased-first512-Conflict-SEP This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6806 - F1: 0.6088 - Accuracy: 0.5914 - Precision: 0.5839 - Recall: 0.6360 ## 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: 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 | F1 | Accuracy | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:| | 0.7027 | 1.0 | 685 | 0.6956 | 0.6018 | 0.5365 | 0.5275 | 0.7003 | | 0.7009 | 2.0 | 1370 | 0.6986 | 0.6667 | 0.5 | 0.5 | 1.0 | | 0.7052 | 3.0 | 2055 | 0.6983 | 0.6667 | 0.5 | 0.5 | 1.0 | | 0.6987 | 4.0 | 2740 | 0.6830 | 0.5235 | 0.5636 | 0.5764 | 0.4795 | | 0.6761 | 5.0 | 3425 | 0.6806 | 0.6088 | 0.5914 | 0.5839 | 0.6360 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
clapika2010/hospital_detection
9b6739319ae49c2bfab53485b27293d0bbdfb4dc
2022-04-18T05:57:28.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers" ]
text-classification
false
clapika2010
null
clapika2010/hospital_detection
5
null
transformers
17,078
Entry not found
EandrewJones/distilbert-base-uncased-finetuned-mediations
cdab35f9479c380e7a8b253bf46571f92c594dc4
2022-04-18T20:09:53.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
EandrewJones
null
EandrewJones/distilbert-base-uncased-finetuned-mediations
5
null
transformers
17,079
Entry not found
dapang/tqa_s2s
025a2b17baf26628edbbc718120a62fc424a2ff6
2022-04-17T06:11:40.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "license:mit" ]
text-generation
false
dapang
null
dapang/tqa_s2s
5
null
transformers
17,080
--- license: mit ---
Cheltone/TESTING
4df97526c67ab81b26f1ce81ef2ff612d6afe011
2022-04-19T01:19:34.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
Cheltone
null
Cheltone/TESTING
5
null
transformers
17,081
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - accuracy - f1 model-index: - name: TESTING results: [] --- <!-- 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. --> # TESTING This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1167 - Precision: 0.9561 - Accuracy: 0.9592 - F1: 0.9592 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|:------:| | 0.5903 | 0.4 | 500 | 0.4695 | 0.7342 | 0.7728 | 0.7890 | | 0.3986 | 0.8 | 1000 | 0.3469 | 0.8144 | 0.8596 | 0.8684 | | 0.2366 | 1.2 | 1500 | 0.1939 | 0.9313 | 0.9260 | 0.9253 | | 0.1476 | 1.6 | 2000 | 0.1560 | 0.9207 | 0.9452 | 0.9465 | | 0.1284 | 2.0 | 2500 | 0.1167 | 0.9561 | 0.9592 | 0.9592 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
benjaminbeilharz/t5-empatheticdialogues
3d26e8503e6747d3b81676bb60a71cce8de57c70
2022-04-17T22:14:58.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
benjaminbeilharz
null
benjaminbeilharz/t5-empatheticdialogues
5
null
transformers
17,082
Entry not found
jason9693/kcelectra-v2022-dev-apeach
f6f4acfaf090d6f9c25ff08ff81ad0fcc2583c8c
2022-04-18T02:33:41.000Z
[ "pytorch", "electra", "text-classification", "ko", "dataset:jason9693/APEACH", "transformers" ]
text-classification
false
jason9693
null
jason9693/kcelectra-v2022-dev-apeach
5
1
transformers
17,083
--- language: ko widget: - text: "코딩을 🐶🍾👟같이 하니까 맨날 장애나잖아 이 🧑‍🦽아" datasets: - jason9693/APEACH ---
crcb/dvs_f
08c846b56dca291c67d749591ba93ea4f6faae28
2022-04-18T13:44:09.000Z
[ "pytorch", "bert", "text-classification", "en", "dataset:crcb/autotrain-data-dvs", "transformers", "autotrain", "co2_eq_emissions" ]
text-classification
false
crcb
null
crcb/dvs_f
5
null
transformers
17,084
--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - crcb/autotrain-data-dvs co2_eq_emissions: 8.758858538967111 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 753223045 - CO2 Emissions (in grams): 8.758858538967111 ## Validation Metrics - Loss: 0.14833936095237732 - Accuracy: 0.9471454508775469 - Precision: 0.5045871559633027 - Recall: 0.4166666666666667 - AUC: 0.8806422686270332 - F1: 0.4564315352697096 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/crcb/autotrain-dvs-753223045 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-dvs-753223045", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-dvs-753223045", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
manueldeprada/ctTREC-distillbert-correct-classifier-trec2020
e11637f3d632e378283e33921e7a5c719fdd1616
2022-04-18T14:17:15.000Z
[ "pytorch", "jax", "distilbert", "text-classification", "transformers" ]
text-classification
false
manueldeprada
null
manueldeprada/ctTREC-distillbert-correct-classifier-trec2020
5
null
transformers
17,085
Entry not found
Gunulhona/tbnymodel_v2
13978e772cb2df4e049e1ed9fbf78d1c17212ac2
2022-04-18T15:37:58.000Z
[ "pytorch", "bart", "text-classification", "transformers" ]
text-classification
false
Gunulhona
null
Gunulhona/tbnymodel_v2
5
null
transformers
17,086
Entry not found
ucabqfe/roberta_PER_io
7d0e91b96161acdc40d01b6a700ad83b936f49e6
2022-04-18T17:56:28.000Z
[ "pytorch", "roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ucabqfe
null
ucabqfe/roberta_PER_io
5
null
transformers
17,087
Entry not found
ShihTing/QA_Leave
3c0226aacac639fc4113c04b882cc0a944ad0e78
2022-04-19T03:41:36.000Z
[ "pytorch", "bert", "text-classification", "unk", "transformers", "autonlp" ]
text-classification
false
ShihTing
null
ShihTing/QA_Leave
5
null
transformers
17,088
# Title 自製QA請假版 --- tags: autonlp language: unk widget: - text: "如果我想請特休,要怎麼使用" - text: "我想請事假" --- 自製QA請假版 訓練與驗證分開 訓練筆67驗證筆23,總類別23,也就是驗證資料每一類各一測試 驗證acc=1.0
xInsignia/autotrain-Online_orders-755323156
2807a6e6308fc286a005384da2308862bde1fa6c
2022-04-19T03:29:18.000Z
[ "pytorch", "distilbert", "text-classification", "en", "dataset:xInsignia/autotrain-data-Online_orders-5cf92320", "transformers", "autotrain", "co2_eq_emissions" ]
text-classification
false
xInsignia
null
xInsignia/autotrain-Online_orders-755323156
5
null
transformers
17,089
--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - xInsignia/autotrain-data-Online_orders-5cf92320 co2_eq_emissions: 2.4120667129093043 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 755323156 - CO2 Emissions (in grams): 2.4120667129093043 ## Validation Metrics - Loss: 0.17826060950756073 - Accuracy: 0.9550898203592815 - Macro F1: 0.8880388927888968 - Micro F1: 0.9550898203592815 - Weighted F1: 0.9528256324309916 - Macro Precision: 0.9093073732635162 - Micro Precision: 0.9550898203592815 - Weighted Precision: 0.9533674643333371 - Macro Recall: 0.8872729481745715 - Micro Recall: 0.9550898203592815 - Weighted Recall: 0.9550898203592815 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/xInsignia/autotrain-Online_orders-755323156 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("xInsignia/autotrain-Online_orders-755323156", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("xInsignia/autotrain-Online_orders-755323156", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
uaritm/lik_neuro_202
ec8eb5c47b487ec1fb4aab3a54dae17ce15aee76
2022-05-11T09:19:32.000Z
[ "pytorch", "t5", "text2text-generation", "ru", "uk", "transformers", "russian", "ukrainian", "license:mit", "autotrain_compatible" ]
text2text-generation
false
uaritm
null
uaritm/lik_neuro_202
5
null
transformers
17,090
--- language: ["ru", "uk"] tags: - russian - ukrainian license: mit --- # The model was trained on the Russian-Ukrainian dataset. Questions-answers of medical subjects (neurology-psychotherapy). The model is not a medical application and it is strongly discouraged to use the model for medical purposes!
anshr/t5-base_supervised_baseline_01
aa6c0ffca47b57aa57f38ac0ab3c63f9bc9d23e1
2022-04-19T20:52:06.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
anshr
null
anshr/t5-base_supervised_baseline_01
5
null
transformers
17,091
Entry not found
Aldraz/distilbert-base-uncased-finetuned-emotion
99128201f415ea496562f203897103ea524ab163
2022-04-20T02:04:55.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
Aldraz
null
Aldraz/distilbert-base-uncased-finetuned-emotion
5
null
transformers
17,092
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- 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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2319 - Accuracy: 0.921 - F1: 0.9214 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3369 | 0.8985 | 0.8947 | | No log | 2.0 | 500 | 0.2319 | 0.921 | 0.9214 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.9.1+cpu - Datasets 2.1.0 - Tokenizers 0.11.6
eslamxm/mT5_multilingual_XLSum-finetuned-ar-wikilingua
fe055602107c713e31599b1c8b4c0e1ef2afc753
2022-04-20T18:31:30.000Z
[ "pytorch", "mt5", "text2text-generation", "dataset:wiki_lingua", "transformers", "summarization", "generated_from_trainer", "model-index", "autotrain_compatible" ]
summarization
false
eslamxm
null
eslamxm/mT5_multilingual_XLSum-finetuned-ar-wikilingua
5
null
transformers
17,093
--- tags: - summarization - generated_from_trainer datasets: - wiki_lingua model-index: - name: mT5_multilingual_XLSum-finetuned-ar-wikilingua results: [] --- <!-- 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. --> # mT5_multilingual_XLSum-finetuned-ar-wikilingua This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the wiki_lingua dataset. It achieves the following results on the evaluation set: - Loss: 3.6903 - Rouge-1: 24.47 - Rouge-2: 7.69 - Rouge-l: 20.04 - Gen Len: 39.64 - Bertscore: 72.63 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 8 - label_smoothing_factor: 0.1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:| | 4.4406 | 1.0 | 5111 | 3.9582 | 22.35 | 6.84 | 18.39 | 34.78 | 71.94 | | 4.0158 | 2.0 | 10222 | 3.8316 | 22.87 | 7.24 | 18.92 | 34.7 | 71.99 | | 3.8626 | 3.0 | 15333 | 3.7695 | 23.65 | 7.5 | 19.6 | 35.53 | 72.31 | | 3.7626 | 4.0 | 20444 | 3.7313 | 24.01 | 7.59 | 19.68 | 38.16 | 72.41 | | 3.6934 | 5.0 | 25555 | 3.7118 | 24.37 | 7.77 | 19.93 | 39.36 | 72.47 | | 3.6421 | 6.0 | 30666 | 3.7016 | 24.48 | 7.8 | 20.07 | 38.58 | 72.58 | | 3.6073 | 7.0 | 35777 | 3.6907 | 24.31 | 7.83 | 20.13 | 38.07 | 72.5 | | 3.5843 | 8.0 | 40888 | 3.6903 | 24.55 | 7.88 | 20.2 | 38.33 | 72.6 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
frozenwalker/T5_pubmedqa_question_generation_preTrained_MedQuad
2d699e987ea3403edbfd844e292a6829574cfba0
2022-04-20T12:23:24.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
frozenwalker
null
frozenwalker/T5_pubmedqa_question_generation_preTrained_MedQuad
5
null
transformers
17,094
Entry not found
sanime/distilbert-base-uncased-finetuned-emotion
1bb515855710f5fcdbcce468ac09a9e163d30e9a
2022-04-20T13:14:47.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
sanime
null
sanime/distilbert-base-uncased-finetuned-emotion
5
null
transformers
17,095
Entry not found
Jeevesh8/feather_berts_19
d426c30c8b842d8acff6db4ae6f2f2f023162824
2022-04-20T13:21:06.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/feather_berts_19
5
null
transformers
17,096
Entry not found
Jeevesh8/feather_berts_20
2a4e8e5088972809b1bf61cad4940bbcd1125450
2022-04-20T13:21:31.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/feather_berts_20
5
null
transformers
17,097
Entry not found
Jeevesh8/feather_berts_28
599554709d6daad126b27e71aeb32d1c923db2a7
2022-04-20T13:24:59.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/feather_berts_28
5
null
transformers
17,098
Entry not found
afbudiman/indobert-distilled-optimized-for-classification
f1e71ca07706aba3ef783969016e88531d8928f1
2022-04-20T13:59:48.000Z
[ "pytorch", "distilbert", "text-classification", "dataset:indonlu", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
afbudiman
null
afbudiman/indobert-distilled-optimized-for-classification
5
null
transformers
17,099
--- license: apache-2.0 tags: - generated_from_trainer datasets: - indonlu metrics: - accuracy - f1 model-index: - name: indobert-distilled-optimized-for-classification results: - task: name: Text Classification type: text-classification dataset: name: indonlu type: indonlu args: smsa metrics: - name: Accuracy type: accuracy value: 0.9023809523809524 - name: F1 type: f1 value: 0.9020516403647337 --- <!-- 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. --> # indobert-distilled-optimized-for-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.5991 - Accuracy: 0.9024 - F1: 0.9021 ## 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: 5.262995179171344e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.2938 | 1.0 | 688 | 0.8433 | 0.8484 | 0.8513 | | 0.711 | 2.0 | 1376 | 0.6408 | 0.8881 | 0.8878 | | 0.4416 | 3.0 | 2064 | 0.7964 | 0.8794 | 0.8793 | | 0.2907 | 4.0 | 2752 | 0.7559 | 0.8897 | 0.8900 | | 0.2065 | 5.0 | 3440 | 0.6892 | 0.8968 | 0.8974 | | 0.1574 | 6.0 | 4128 | 0.6881 | 0.8913 | 0.8906 | | 0.1131 | 7.0 | 4816 | 0.6224 | 0.8984 | 0.8982 | | 0.0865 | 8.0 | 5504 | 0.6312 | 0.8976 | 0.8970 | | 0.0678 | 9.0 | 6192 | 0.6187 | 0.8992 | 0.8989 | | 0.0526 | 10.0 | 6880 | 0.5991 | 0.9024 | 0.9021 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1