license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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mit | ['summarization', 'arxiv:2005.00661', 'arxiv:2111.09525', 'arxiv:2112.08542', 'arxiv:2104.04302', 'arxiv:2109.09209'] | false | German news title gen This is a model for the task of news headline generation in German. While this task is very similar to summarization, there remain differences like length, structure, and language style, which cause state-of-the-art summarization models not to be suited best for headline generation and demand ... | dbff5f975a84eca87829d6e1956b840b |
mit | ['summarization', 'arxiv:2005.00661', 'arxiv:2111.09525', 'arxiv:2112.08542', 'arxiv:2104.04302', 'arxiv:2109.09209'] | false | Dataset & preprocessing The model was finetuned on a corpus of news articles from [BR24](https://www.br.de/) published between 2015 and 2021. The texts are in german language and cover a range of different news topics like politics, sports, and culture, with a focus on topics that are relevant to the people living in... | 59bf5112a8aed81b9efc3b55fc021135 |
mit | ['summarization', 'arxiv:2005.00661', 'arxiv:2111.09525', 'arxiv:2112.08542', 'arxiv:2104.04302', 'arxiv:2109.09209'] | false | Training After multiple test runs of finetuning the present model was further trained using the following parameters: - foundation-model: mT5-base - input_prefix: "summarize: " - num_train_epochs: 10 - learning_rate: 5e-5 - warmup_ratio: 0.3 - lr_scheduler_type: constant_with_warmup - per_device_train_batch_size: 3 -... | 2198b6bba913ad8a9f76dd1b168f22a7 |
mit | ['summarization', 'arxiv:2005.00661', 'arxiv:2111.09525', 'arxiv:2112.08542', 'arxiv:2104.04302', 'arxiv:2109.09209'] | false | Usage Because the model was fine tuned on mT5, the usage is analogous to the T5 model ([see docs](https://huggingface.co/docs/transformers/model_doc/t5)). Another option for using the model for inference is the huggingface [summarization pipeline](https://huggingface.co/docs/transformers/v4.23.1/en/main_classes/pipel... | 17de310d0e8e2f1819f04614be910980 |
mit | ['summarization', 'arxiv:2005.00661', 'arxiv:2111.09525', 'arxiv:2112.08542', 'arxiv:2104.04302', 'arxiv:2109.09209'] | false | transformers.SummarizationPipeline). In both cases the prefix `summarize: ` has to be added to the input texts. For obtaining higher quality headlines it is recommended to increase the beam size for genereation. In the evaluations conducted for this model a beam size of 5 was used. | bff569275c78ca8764868248b1bb808f |
mit | ['summarization', 'arxiv:2005.00661', 'arxiv:2111.09525', 'arxiv:2112.08542', 'arxiv:2104.04302', 'arxiv:2109.09209'] | false | Example: Direct model evaluation ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model_id = "" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForSeq2SeqLM.from_pretrained(model_id) text = "Als Reaktion auf die Brandserie wurde am Mittwoch bei der Kriminalpolizei Würzbur... | bf4ed2988861bb020417f28666b778f7 |
mit | ['summarization', 'arxiv:2005.00661', 'arxiv:2111.09525', 'arxiv:2112.08542', 'arxiv:2104.04302', 'arxiv:2109.09209'] | false | Example: Model evaluation using huggingface pipeline ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline model_id = "" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForSeq2SeqLM.from_pretrained(model_id) headline_generator = pipeline( "summarization", model... | bc4c6175aa3b4171311dcaed3a092976 |
mit | ['summarization', 'arxiv:2005.00661', 'arxiv:2111.09525', 'arxiv:2112.08542', 'arxiv:2104.04302', 'arxiv:2109.09209'] | false | Limitations Like most state-of-the-art summarization models this model has issues with the factuality of the generated texts [^factuality]. **It is therefore strongly advised having a human fact-check the generated headlines.** An analysis of possible biases reproduced by the present model, regardless of whether they... | 05c86cbfe3eeb59f6de5162868c2d525 |
mit | ['summarization', 'arxiv:2005.00661', 'arxiv:2111.09525', 'arxiv:2112.08542', 'arxiv:2104.04302', 'arxiv:2109.09209'] | false | Quantitative | model | Rouge1 | Rouge2 | RougeL | RougeLsum | |-|-|-|-|-| | [T-Systems-onsite/mt5-small-sum-de-en-v2](https://huggingface.co/T-Systems-onsite/mt5-small-sum-de-en-v2)| 0.107 | 0.0297 | 0.098 | 0.098 | | aiautomationlab/german-news-title-gen-mt5 | 0.3131 | 0.0873 | 0.1997 | 0.1997 | For evaluating the ... | e9b197e814d9637af96adfd5900aca74 |
mit | ['summarization', 'arxiv:2005.00661', 'arxiv:2111.09525', 'arxiv:2112.08542', 'arxiv:2104.04302', 'arxiv:2109.09209'] | false | Qualitative A qualitative evaluation conducted by members of the BR AI + Automation Lab showed that the model succeeds in producing headlines that match the language and style of news headlines, but also confirms that there are issues with the factual consistency common to state-of-the-art summarization models. | ed16ddf9f025c3349247d8959fd3e459 |
mit | ['summarization', 'arxiv:2005.00661', 'arxiv:2111.09525', 'arxiv:2112.08542', 'arxiv:2104.04302', 'arxiv:2109.09209'] | false | Future work Future work on this model will focus on generating headlines with higher factual consistency regarding the text. Ideas to achieve this goal include: - Use of coreference resolution as additional preprocessing step for making the relations within the text more explicit to the model. - Use of contrastive le... | 29f5a5639f1219502d45f75ee95e1a29 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_accent_us-0_england-10_s35 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | c9fc05697dca10e54e7b8485e8262c19 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | allenai-specter This model is a conversion of the [AllenAI SPECTER](https://github.com/allenai/specter) model to [sentence-transformers](https://www.SBERT.net). It can be used to map the titles & abstracts of scientific publications to a vector space such that similar papers are close. | 4a0524a1172b30f894c94d11e3fda42d |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | fcf998e58ede5dee95d383c265bffd96 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTo... | 088610fb4faa11de2d1e395528e7031b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/allenai-specter) | cb1ad254871840f7229d6c965a1479b7 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mea... | 4c38572999d39c83c81d38ff614bd413 |
apache-2.0 | ['generated_from_keras_callback'] | false | Rocketknight1/t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.7172 - Validation Loss: 2.3977 - Train Rouge1: 28.7469 - Train Rouge2: 7.9005 - Train Rougel: 22.5917 ... | a99f785e1abb99714db2e040be696b15 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 2.7172 | 2.3977 | 28.7469 | 7.9005 ... | b04ecde6318446ded847e7b6b46fb057 |
mit | [] | false | minecraft-concept-art on Stable Diffusion This is the `<concept>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You ... | 265da6c4112e5620ba2d4750febf852f |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-meta-5-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4797 - Accuracy: 0.28 | 30f9763f9b183a95395d05a757ffc8e5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-stsb 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: 0.5634 - Pearson: 0.8680 - Spearmanr: 0.8652 | cb5f1028db21bc8d80320d2536833d11 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | No log | 1.0 | 360 | 0.6646 | 0.8516 | 0.8494 | | 1.0238 | 2.0 | 720 | 0.5617 | 0.8666 | 0.8637 | | 0.3952 ... | c5ba3bd87cc683ee4e90ab9693725a31 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'cs:go', 'topview', 'map generator', 'layout', 'layout generator', 'map', 'csgo', 'improved layout', 'radar'] | false | CSGO Minimap Layout Generation  This is an improved AI model of my previous model trained on CS:GO's radar top view images of many maps which can now produce custom map layouts in seconds. This model does not p... | 50dc7cf032d48264919c9c91744baf3b |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'cs:go', 'topview', 'map generator', 'layout', 'layout generator', 'map', 'csgo', 'improved layout', 'radar'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler impor... | 17d3f88c6125614a29c34db17e894217 |
apache-2.0 | ['generated_from_keras_callback'] | false | malay-patel/bert-ww-finetuned-squad This model is a fine-tuned version of [bert-large-cased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1766 - Train End L... | 5a8c9ee5d88f4c26611479b1bab79188 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16638, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 83df856b84d7ef5ada97417de4fa06ea |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:-----:| | 0.5635 | 0.8374 | 0.7992 | 0 | | 0.3369 | 0.8987 | 0.8695 ... | 32c31864792b4776f278f00c027d6902 |
mit | ['generated_from_trainer'] | false | bart-pt-asqa-ob This model is a fine-tuned version of [vblagoje/bart_lfqa](https://huggingface.co/vblagoje/bart_lfqa) on the [ASQA](https://huggingface.co/datasets/din0s/asqa) dataset. It achieves the following results on the evaluation set: - Loss: 1.6901 - Rougelsum: 20.7527 | 386a26f1cda951f7deb74f9494322df7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:---------:| | No log | 1.0 | 355 | 1.6295 | 17.7502 | | 1.6407 | 2.0 | 710 | 1.6144 | 18.5897 | | 1.4645 | 3.0 | 1065 | 1.6222 ... | 1896ac1aa4f9ba14624f02537dfe8459 |
mit | ['spacy', 'token-classification'] | false | | Feature | Description | | --- | --- | | **Name** | `en_reciparse_model` | | **Version** | `0.0.0` | | **spaCy** | `>=3.3.1,<3.4.0` | | **Default Pipeline** | `tok2vec`, `ner` | | **Components** | `tok2vec`, `ner` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | n/a | |... | 28c36e756401c1dbabe557af644cac0f |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | atrevidoantonio1 Dreambooth model trained by atrevidoantonio with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/... | 6bb271c0364f0a2f02c7c079196706f4 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-Indonesian This is the model for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) model on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice). When using this model, make sure th... | 43b9b1079f36f6847c23cc5e3494acda |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "id", split="test[:2%]") processor = Wav2Vec2Processor.from_pre... | 7b85264495d32937ee2554fec2f04a0f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Indonesian test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "id", split="test") w... | ccce3ee21a2c98109c075e77faa8684b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(lo... | ec9b03cfef75a57e52bfe52f896da0ca |
apache-2.0 | ['generated_from_trainer'] | false | QA-distilbert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.5374 | a1251e5a5c015d6e28a4a79e704d722b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 2.0457 | | 2.5775 | 2.0 | 500 | 1.6041 | | 2.5775 | 3.0 | 750 | 1.5374 | | bf4f3185b72751b182a4c5d065d666a3 |
mit | [] | false | egorey on Stable Diffusion This is the `<gorey>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train yo... | 6e4031d74af44365d5e677017d172c57 |
apache-2.0 | ['translation'] | false | opus-mt-lu-en * source languages: lu * target languages: en * OPUS readme: [lu-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lu-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 2d09a6c8e8b504d5ac08e128b04e09a8 |
mit | ['generated_from_keras_callback'] | false | PromptGenerator_5_topic This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 10.6848 - Validation Loss: 10.6672 - Epoch: 4 | 6f2b4955cdb42a3aa4b6adc5d1516f63 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | 002fcd3fa5765015509cb6372cca4e2f |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.6864 | 10.6743 | 0 | | 10.7045 | 10.6736 | 1 | | 10.7114 | 10.6722 | 2 | | 10.7082 | 10.6701 | 3 | | 10.6848 | 10.6672 | 4 | | cd6ba1c4a8ef25b58b492a38ea4d3d79 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_mrpc_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5213 - Accuracy: 0.6740 - F1: 0.7787 - Combined Score: 0.7264... | 734d757a2e9406da63d1cdfe6ebd866d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6368 | 1.0 | 29 | 0.5564 | 0.6838 | 0.8122 | 0.7480 | | 0.6099 | 2.0 | 58 | 0.55... | 332a730431e95538ee4a9d58e74f88d2 |
apache-2.0 | ['pytorch', 'image-to-text'] | false | Model fine-tuning 🏋️ The model has been finetuned for 10 epochs on the scenes captions of the [HL]() dataset (available on 🤗 HUB: [michelecafagna26/hl](https://huggingface.co/datasets/michelecafagna26/hl)) | 86178c3e57a25f3b5ddf5fddfe2e86b0 |
apache-2.0 | ['pytorch', 'image-to-text'] | false | Test set metrics 📈 Obtained with beam size 5 and max length 20 | Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | METEOR | ROUGE-L | CIDEr | SPICE | |--------|--------|--------|--------|--------|---------|-------|-------| | 0.68 | 0.55 | 0.45 | 0.36 | 0.36 | 0.63 | 1.42 | 0.40 | | a5adc566b16b844bf1bf3d029dd40b4b |
apache-2.0 | ['pytorch', 'image-to-text'] | false | Feature extraction ⛏️ This model has a separate Visualbackbone used to extract features. More info about: - the model: [michelecafagna26/vinvl_vg_x152c4](https://huggingface.co/michelecafagna26/vinvl_vg_x152c4) - the usage: [michelecafagna26/vinvl-visualbackbone](https://github.com/michelecafagna26/vinvl-visualbackbo... | 5b2e1942cf916fc8cb374a41af18478d |
apache-2.0 | ['pytorch', 'image-to-text'] | false | Quick start: 🚀 ```python from transformers.pytorch_transformers import BertConfig, BertTokenizer from oscar.modeling.modeling_bert import BertForImageCaptioning from oscar.wrappers import OscarTensorizer ckpt = "path/to/the/checkpoint" device = "cuda" if torch.cuda.is_available() else "cpu" | ca25c97e06ceb20f8c87a0b41f7b2995 |
apache-2.0 | ['pytorch', 'image-to-text'] | false | labels are usually extracted by the features extractor labels = [['boat', 'boat', 'boat', 'bottom', 'bush', 'coat', 'deck', 'deck', 'deck', 'dock', 'hair', 'jacket']] inputs = tensorizer.encode(visual_features, labels=labels) outputs = model(**inputs) pred = tensorizer.decode(outputs) | 6f95370ac0d41ec7028a737787c264d2 |
apache-2.0 | ['pytorch', 'image-to-text'] | false | Citations 🧾 VinVL model finetuned on scenes descriptions: ```BibTeX @inproceedings{cafagna-etal-2022-understanding, title = "Understanding Cross-modal Interactions in {V}{\&}{L} Models that Generate Scene Descriptions", author = "Cafagna, Michele and Deemter, Kees van and Gatt, Albert", bo... | 3b2cb87cf185019ae027969dd8f7a216 |
creativeml-openrail-m | ['text-to-image'] | false | novgoranstefanovski on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | a0a9b7ecc8c62a91ee262231fa363dc8 |
creativeml-openrail-m | ['text-to-image'] | false | Model by Buntopsih This your the Stable Diffusion model fine-tuned the novgoranstefanovski concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **Robert, retro, Greg, Kim** You can also train your own concepts and upload them to the library by using [the fast-Drema... | 515f21eeee05528ede08e54f653361ef |
apache-2.0 | ['Quality Estimation', 'microtransquest'] | false | Using Pre-trained Models ```python from transquest.algo.word_level.microtransquest.run_model import MicroTransQuestModel import torch model = MicroTransQuestModel("xlmroberta", "TransQuest/microtransquest-en_cs-it-smt", labels=["OK", "BAD"], use_cuda=torch.cuda.is_available()) source_tags, target_tags = model.predic... | 626579a8ed7a519c97cbb026ebbe4a37 |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper3_0005 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.1592 - Train Accuracy: 0.0175 - Validation Loss: 2.8062 - Validation Accuracy: 0.0199 - Epoch: 4 | 35ae3de757ad0c6cf40620afd9123682 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 5.0832 | 0.0116 | 4.4298 | 0.0124 | 0 | | 4.3130 | 0.0131 | 4.0733 | 0.0141 ... | 7e5c526648b312cb5a68bf13b881aefc |
mit | ['generated_from_trainer'] | false | xlm-roberta-large-finetuned-TRAC-DS This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0992 - Accuracy: 0.3342 - Precision: 0.1114 - Recall: 0.3333 - F1: 0.1670 | c3ea44d35a8649b28e28697a34f81a1c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.1187640010910775e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 | 9fb32feecea71f959584b4539dc1b322 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.1358 | 0.25 | 612 | 1.1003 | 0.4436 | 0.1479 | 0.3333 | 0.2049 | | 1.1199 | 0.5 |... | 7b66daba32aa65235eb3fd89de9e4fcb |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3105 - Accuracy: 0.87 - F1: 0.8713 | 16447300c9ed678ef8e6585d1cab8c1b |
apache-2.0 | ['translation'] | false | opus-mt-nso-fi * source languages: nso * target languages: fi * OPUS readme: [nso-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | fcab20fc279fb6c462eb2fe73c33a0b7 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | - **Release ✨v1✨** (January 18th, 2023) *[Full-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1), [sharded](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1-sharded), [half-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1-float16), and [mesh-transformers-jax](https://huggingface.co/NbAiL... | 917ce150c1ae9c4d20d5daba5ef9233a |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Model Description NB-GPT-J-6B is a Norwegian finetuned version of GPT-J 6B, a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters (6 billion parameters). ... | 4125cadcaeb3d1a0c77425f7ee9e11fb |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Training procedure This model was finetuned for 130 billion tokens over 1,000,000 steps on a TPU v3-8 VM. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly. | a95a2e8768125ef176cafa8fc6da853c |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Intended Use and Limitations NB-GPT-J-6B learns an inner representation of the Norwegian language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating text from a prompt. | b420cf3ac087982e69c1f9a22c82d1ee |
apache-2.0 | ['pytorch', 'causal-lm'] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-gpt-j-6B") model = AutoModelForCausalLM.from_pretrained("NbAiLab/nb-gpt-j-6B") ``` | 16d89ce0acf4a5080146eaefbf0879e9 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Limitations and Biases As the original GPT-J model, the core functionality of NB-GPT-J-6B is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting NB-GPT-J-6B it is important to remember that t... | 322be1125cf00c66769ca081111b8ae5 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | BibTeX entry To cite this model or the corpus used: ```bibtex @inproceedings{kummervold2021operationalizing, title={Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model}, author={Kummervold, Per E and De la Rosa, Javier and Wetjen, Freddy and Brygfjeld, Svein Arne}, booktitle=... | 2235712d8bef3d89d34396f0bc7ba686 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Disclaimer The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems b... | 551abd4086df440682efc55b0c3c2929 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/), as well as the Cloud TPU team for providing early access to the [Cloud TPU VM](https://cloud.google.com/blog/products/compute/introducing-cloud... | 1e49e4b83ba38c0ed52249afbe70ac62 |
apache-2.0 | ['MiniLMv2'] | false | Cross-Encoder for Natural Language Inference This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. | 625238f7035db288fd825087f2a683c1 |
apache-2.0 | ['MiniLMv2'] | false | Usage Pre-trained models can be used like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('cross-encoder/nli-MiniLM2-L6-H768') scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving ... | 91f61e394ab373156f99eb5e9b5f733e |
apache-2.0 | ['MiniLMv2'] | false | Usage with Transformers AutoModel You can use the model also directly with Transformers library (without SentenceTransformers library): ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-MiniLM... | 75e513b61ed4bce9a9d9b5cd2928de8b |
apache-2.0 | ['MiniLMv2'] | false | Zero-Shot Classification This model can also be used for zero-shot-classification: ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-MiniLM2-L6-H768') sent = "Apple just announced the newest iPhone X" candidate_labels = ["technology", "sports", "po... | 6e5903a8bb0234c2621e3d461ac09fcb |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_unispeech-ml_s569 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using... | c7e5038d0f927270cd02c8e3763ba00d |
apache-2.0 | [] | false | BERT large model (cased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is cased: it makes a difference between engli... | 70b07327f528d802579deb8ec807be45 |
apache-2.0 | [] | false | Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs ... | 9493d89b5c0994727975445dab21c731 |
apache-2.0 | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-large-cased') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] Hello I'm a male model. [SEP]", "score... | bdf085db951e4114701cfbb6c6d4bdd9 |
apache-2.0 | [] | false | Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-large-cased') >>> unmasker("The man worked as a [MASK].") [ { ... | c42ee4b83079e9c85eedb59c566c7bee |
apache-2.0 | [] | false | Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Model | SQUAD 1.1 F1/EM | Multi NLI Accuracy ---------------------------------------- | :-------------: | :----------------: BERT-Large, Cased (Original) | 91.5/84.8 ... | 3e59bd4807e0c4bc239090e99d780c64 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-stac-local This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9746 - Wer: 0.7828 - Cer: 0.3202 | 150fd65fa373cf6dc7c3f641d716ac9c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 2.0603 | 1.0 | 2369 | 2.1282 | 0.9517 | 0.5485 | | 1.6155 | 2.0 | 4738 | 1.6196 | 0.9060 | 0.4565 | | 1.3462 | 3.0... | 1402c47cbeee9f432cc778b4b4b7edc5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 84f901044b9617b3ee8da90dbd114b70 |
mit | [] | false | ivan grohar on Stable Diffusion This is the `<ivan-grohar>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can al... | 0f24114fd9356a06de347fbc694fa3e3 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qnli-target-glue-mrpc This model is a fine-tuned version of [muhtasham/small-mlm-glue-qnli](https://huggingface.co/muhtasham/small-mlm-glue-qnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9217 - Accuracy: 0.7770 - F1: 0.8455 | e0fe189bf710ed2ddc2c73daa7cdd519 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3905 | 4.35 | 500 | 0.7540 | 0.7892 | 0.8608 | | 0.0675 | 8.7 | 1000 | 1.4012 | 0.7892 | 0.8608 | | 0.0274 |... | 106d40fe3faa0476591bcb55947803c7 |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | segformer-b0-finetuned-warehouse-part-1-V2 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the jakka/warehouse_part1 dataset. It achieves the following results on the evaluation set: - Loss: 0.2737 - Mean Iou: 0.7224 - Mean Accuracy: 0.8119 - Overall Accuracy: 0.9668 - P... | a2ef600c22b8ffb797af198d9741fe1f |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-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 - num_epochs: 20 | 5670cf5713b687a2f1064c082c559ea6 |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou ... | 079a90f8c843f647073bd1dbe9a76b70 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion-lr-0.0006-wd-0003 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.4198 - Accuracy: 0.8875 - F1: 0.8889 | 03e9bb58c6c8ba8a585cdad1c73e2f92 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0006 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP | abd6ebba83700b1a399ae0203f9855a0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.1911 | 1.0 | 125 | 0.6098 | 0.808 | 0.7921 | | 0.4819 | 2.0 | 250 | 0.4198 | 0.8875 | 0.8889 | | 5dc92ae75362b7465b0d09d880e1e1b8 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-sst2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9312 - Accuracy: 0.876 | 9d1dacd0a336a3a4f0acf1ccec8f8edb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 1.0209 | 0.836 | | No log | 2.0 | 250 | 1.0430 | 0.85 | | No log | 3.0 | 375 | 0.9312 | 0.... | 8b6d42363885eb78fa22ae9c3c043572 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1356 - F1: 0.8600 | 698ed5a3dae2378c73bd8ef2c756858b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2525 | 1.0 | 525 | 0.1673 | 0.8294 | | 0.1298 | 2.0 | 1050 | 0.1381 | 0.8510 | | 0.0839 | 3.0 | 1575 | 0.1356 | 0.8600 | ... | 9e2db99934d2cde5752ddca7f52e3faf |
gpl-3.0 | ['Slovak GPT-J', 'pytorch', 'causal-lm'] | false | Slovak GPT-J-405M Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant [Slovak GPT-J-162M](https://huggingface.co/Milos/slovak-gpt-j-162M). Since then a larger [Slovak GPT-J-1.4B](https://huggingface.co/Milos/slovak-gpt-j-1.4B) was released. | 4564601f8a79e254b99ae4ff7eaffa20 |
gpl-3.0 | ['Slovak GPT-J', 'pytorch', 'causal-lm'] | false | Model Description Model is based on [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax/) and has over 405M trainable parameters. <figure> | Hyperparameter | Value | |--------... | d95e87f4f16cf9dbe3334b4dc811b5de |
gpl-3.0 | ['Slovak GPT-J', 'pytorch', 'causal-lm'] | false | Training data Slovak GPT-J models were trained on a privately collected dataset consisting of predominantly Slovak text spanning different categories, e.g. web, news articles or even biblical texts - in total, over 40GB of text data was used to train this model. The dataset was preprocessed and cleaned in a specific ... | a88c0ccbbf2e9ffe292dd80e3c4e1d16 |
gpl-3.0 | ['Slovak GPT-J', 'pytorch', 'causal-lm'] | false | Intended Use Same as the original GPT-J, Slovak GPT-J learns an inner representation of the language that can be used to extract features useful for downstream tasks, however, the intended use is text generation from a prompt. | 51bcd6bcefd79a3b0f49cca02fb4c6ca |
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