index int64 0 22.3k | modelId stringlengths 8 111 | label sequence | readme stringlengths 0 385k |
|---|---|---|---|
0 | distilbert-base-uncased-finetuned-sst-2-english | [
"NEGATIVE",
"POSITIVE"
] | ---
language: en
license: apache-2.0
datasets:
- sst2
- glue
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: sst2
split: validation
metrics:
- type: accuracy
value: 0.9105504587155964
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2YyOGMxYjY2Y2JhMjkxNjIzN2FmMjNiNmM2ZWViNGY3MTNmNWI2YzhiYjYxZTY0ZGUyN2M1NGIxZjRiMjQwZiIsInZlcnNpb24iOjF9.uui0srxV5ZHRhxbYN6082EZdwpnBgubPJ5R2-Wk8HTWqmxYE3QHidevR9LLAhidqGw6Ih93fK0goAXncld_gBg
- type: precision
value: 0.8978260869565218
name: Precision
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzgwYTYwYjA2MmM0ZTYwNDk0M2NmNTBkZmM2NGNhYzQ1OGEyN2NkNDQ3Mzc2NTQyMmZiNDJiNzBhNGVhZGUyOSIsInZlcnNpb24iOjF9.eHjLmw3K02OU69R2Au8eyuSqT3aBDHgZCn8jSzE3_urD6EUSSsLxUpiAYR4BGLD_U6-ZKcdxVo_A2rdXqvUJDA
- type: recall
value: 0.9301801801801802
name: Recall
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGIzM2E3MTI2Mzc2MDYwNmU3ZTVjYmZmZDBkNjY4ZTc5MGY0Y2FkNDU3NjY1MmVkNmE3Y2QzMzAwZDZhOWY1NiIsInZlcnNpb24iOjF9.PUZlqmct13-rJWBXdHm5tdkXgETL9F82GNbbSR4hI8MB-v39KrK59cqzFC2Ac7kJe_DtOeUyosj34O_mFt_1DQ
- type: auc
value: 0.9716626673402374
name: AUC
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDM0YWIwZmQ4YjUwOGZmMWU2MjI1YjIxZGQ2MzNjMzRmZmYxMzZkNGFjODhlMDcyZDM1Y2RkMWZlOWQ0MWYwNSIsInZlcnNpb24iOjF9.E7GRlAXmmpEkTHlXheVkuL1W4WNjv4JO3qY_WCVsTVKiO7bUu0UVjPIyQ6g-J1OxsfqZmW3Leli1wY8vPBNNCQ
- type: f1
value: 0.9137168141592922
name: F1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGU4MjNmOGYwZjZjMDQ1ZTkyZTA4YTc1MWYwOTM0NDM4ZWY1ZGVkNDY5MzNhYTQyZGFlNzIyZmUwMDg3NDU0NyIsInZlcnNpb24iOjF9.mW5ftkq50Se58M-jm6a2Pu93QeKa3MfV7xcBwvG3PSB_KNJxZWTCpfMQp-Cmx_EMlmI2siKOyd8akYjJUrzJCA
- type: loss
value: 0.39013850688934326
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTZiNzAyZDc0MzUzMmE1MGJiN2JlYzFiODE5ZTNlNGE4MmI4YzRiMTc2ODEzMTUwZmEzOTgxNzc4YjJjZTRmNiIsInZlcnNpb24iOjF9.VqIC7uYC-ZZ8ss9zQOlRV39YVOOLc5R36sIzCcVz8lolh61ux_5djm2XjpP6ARc6KqEnXC4ZtfNXsX2HZfrtCQ
- task:
type: text-classification
name: Text Classification
dataset:
name: sst2
type: sst2
config: default
split: train
metrics:
- type: accuracy
value: 0.9885521685548412
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2I3NzU3YzhmMDkxZTViY2M3OTY1NmI0ZTdmMDQxNjNjYzJiZmQxNzczM2E4YmExYTY5ODY0NDBkY2I4ZjNkOCIsInZlcnNpb24iOjF9.4Gtk3FeVc9sPWSqZIaeUXJ9oVlPzm-NmujnWpK2y5s1Vhp1l6Y1pK5_78wW0-NxSvQqV6qd5KQf_OAEpVAkQDA
- type: precision
value: 0.9881965062029833
name: Precision Macro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDdlZDMzY2I3MTAwYTljNmM4MGMyMzU2YjAzZDg1NDYwN2ZmM2Y5OWZhMjUyMGJiNjY1YmZiMzFhMDI2ODFhNyIsInZlcnNpb24iOjF9.cqmv6yBxu4St2mykRWrZ07tDsiSLdtLTz2hbqQ7Gm1rMzq9tdlkZ8MyJRxtME_Y8UaOG9rs68pV-gKVUs8wABw
- type: precision
value: 0.9885521685548412
name: Precision Micro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjFlYzAzNmE1YjljNjUwNzBjZjEzZDY0ZDQyMmY5ZWM2OTBhNzNjYjYzYTk1YWE1NjU3YTMxZDQwOTE1Y2FkNyIsInZlcnNpb24iOjF9.jnCHOkUHuAOZZ_ZMVOnetx__OVJCS6LOno4caWECAmfrUaIPnPNV9iJ6izRO3sqkHRmxYpWBb-27GJ4N3LU-BQ
- type: precision
value: 0.9885639626373408
name: Precision Weighted
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGUyODFjNjBlNTE2MTY3ZDAxOGU1N2U0YjUyY2NiZjhkOGVmYThjYjBkNGU3NTRkYzkzNDQ2MmMwMjkwMWNiMyIsInZlcnNpb24iOjF9.zTNabMwApiZyXdr76QUn7WgGB7D7lP-iqS3bn35piqVTNsv3wnKjZOaKFVLIUvtBXq4gKw7N2oWxvWc4OcSNDg
- type: recall
value: 0.9886145346602994
name: Recall Macro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTU1YjlhODU3YTkyNTdiZDcwZGFlZDBiYjY0N2NjMGM2NTRiNjQ3MDNjNGMxOWY2ZGQ4NWU1YmMzY2UwZTI3YSIsInZlcnNpb24iOjF9.xaLPY7U-wHsJ3DDui1yyyM-xWjL0Jz5puRThy7fczal9x05eKEQ9s0a_WD-iLmapvJs0caXpV70hDe2NLcs-DA
- type: recall
value: 0.9885521685548412
name: Recall Micro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODE0YTU0MDBlOGY4YzU0MjY5MzA3OTk2OGNhOGVkMmU5OGRjZmFiZWI2ZjY5ODEzZTQzMTI0N2NiOTVkNDliYiIsInZlcnNpb24iOjF9.SOt1baTBbuZRrsvGcak2sUwoTrQzmNCbyV2m1_yjGsU48SBH0NcKXicidNBSnJ6ihM5jf_Lv_B5_eOBkLfNWDQ
- type: recall
value: 0.9885521685548412
name: Recall Weighted
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWNkNmM0ZGRlNmYxYzIwNDk4OTI5MzIwZWU1NzZjZDVhMDcyNDFlMjBhNDQxODU5OWMwMWNhNGEzNjY3ZGUyOSIsInZlcnNpb24iOjF9.b15Fh70GwtlG3cSqPW-8VEZT2oy0CtgvgEOtWiYonOovjkIQ4RSLFVzVG-YfslaIyfg9RzMWzjhLnMY7Bpn2Aw
- type: f1
value: 0.9884019815052447
name: F1 Macro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmM4NjQ5Yjk5ODRhYTU1MTY3MmRhZDBmODM1NTg3OTFiNWM4NDRmYjI0MzZkNmQ1MzE3MzcxODZlYzBkYTMyYSIsInZlcnNpb24iOjF9.74RaDK8nBVuGRl2Se_-hwQvP6c4lvVxGHpcCWB4uZUCf2_HoC9NT9u7P3pMJfH_tK2cpV7U3VWGgSDhQDi-UBQ
- type: f1
value: 0.9885521685548412
name: F1 Micro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDRmYWRmMmQ0YjViZmQxMzhhYTUyOTE1MTc0ZDU1ZjQyZjFhMDYzYzMzZDE0NzZlYzQyOTBhMTBhNmM5NTlkMiIsInZlcnNpb24iOjF9.VMn_psdAHIZTlW6GbjERZDe8MHhwzJ0rbjV_VJyuMrsdOh5QDmko-wEvaBWNEdT0cEKsbggm-6jd3Gh81PfHAQ
- type: f1
value: 0.9885546181087554
name: F1 Weighted
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjUyZWFhZDZhMGQ3MzBmYmRiNDVmN2FkZDBjMjk3ODk0OTAxNGZkMWE0NzU5ZjI0NzE0NGZiNzM0N2Y2NDYyOSIsInZlcnNpb24iOjF9.YsXBhnzEEFEW6jw3mQlFUuIrW7Gabad2Ils-iunYJr-myg0heF8NEnEWABKFE1SnvCWt-69jkLza6SupeyLVCA
- type: loss
value: 0.040652573108673096
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTc3YjU3MjdjMzkxODA5MjU5NGUyY2NkMGVhZDg3ZWEzMmU1YWVjMmI0NmU2OWEyZTkzMTVjNDZiYTc0YjIyNCIsInZlcnNpb24iOjF9.lA90qXZVYiILHMFlr6t6H81Oe8a-4KmeX-vyCC1BDia2ofudegv6Vb46-4RzmbtuKeV6yy6YNNXxXxqVak1pAg
---
# DistilBERT base uncased finetuned SST-2
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
## Model Details
**Model Description:** This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned on SST-2.
This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7).
- **Developed by:** Hugging Face
- **Model Type:** Text Classification
- **Language(s):** English
- **License:** Apache-2.0
- **Parent Model:** For more details about DistilBERT, we encourage users to check out [this model card](https://huggingface.co/distilbert-base-uncased).
- **Resources for more information:**
- [Model Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification)
- [DistilBERT paper](https://arxiv.org/abs/1910.01108)
## How to Get Started With the Model
Example of single-label classification:
```python
import torch
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]
```
## Uses
#### Direct Use
This model can be used for topic classification. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.
#### Misuse and Out-of-scope Use
The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
## Risks, Limitations and Biases
Based on a few experimentations, we observed that this model could produce biased predictions that target underrepresented populations.
For instance, for sentences like `This film was filmed in COUNTRY`, this binary classification model will give radically different probabilities for the positive label depending on the country (0.89 if the country is France, but 0.08 if the country is Afghanistan) when nothing in the input indicates such a strong semantic shift. In this [colab](https://colab.research.google.com/gist/ageron/fb2f64fb145b4bc7c49efc97e5f114d3/biasmap.ipynb), [Aurélien Géron](https://twitter.com/aureliengeron) made an interesting map plotting these probabilities for each country.
<img src="https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/map.jpeg" alt="Map of positive probabilities per country." width="500"/>
We strongly advise users to thoroughly probe these aspects on their use-cases in order to evaluate the risks of this model. We recommend looking at the following bias evaluation datasets as a place to start: [WinoBias](https://huggingface.co/datasets/wino_bias), [WinoGender](https://huggingface.co/datasets/super_glue), [Stereoset](https://huggingface.co/datasets/stereoset).
# Training
#### Training Data
The authors use the following Stanford Sentiment Treebank([sst2](https://huggingface.co/datasets/sst2)) corpora for the model.
#### Training Procedure
###### Fine-tuning hyper-parameters
- learning_rate = 1e-5
- batch_size = 32
- warmup = 600
- max_seq_length = 128
- num_train_epochs = 3.0
|
1 | roberta-base-openai-detector | [
"Fake",
"Real"
] | ---
language: en
license: mit
tags:
- exbert
datasets:
- bookcorpus
- wikipedia
---
# RoBERTa Base OpenAI Detector
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Citation Information](#citation-information)
- [Model Card Authors](#model-card-author)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
**Model Description:** RoBERTa base OpenAI Detector is the GPT-2 output detector model, obtained by fine-tuning a RoBERTa base model with the outputs of the 1.5B-parameter GPT-2 model. The model can be used to predict if text was generated by a GPT-2 model. This model was released by OpenAI at the same time as OpenAI released the weights of the [largest GPT-2 model](https://huggingface.co/gpt2-xl), the 1.5B parameter version.
- **Developed by:** OpenAI, see [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector) and [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for full author list
- **Model Type:** Fine-tuned transformer-based language model
- **Language(s):** English
- **License:** MIT
- **Related Models:** [RoBERTa base](https://huggingface.co/roberta-base), [GPT-XL (1.5B parameter version)](https://huggingface.co/gpt2-xl), [GPT-Large (the 774M parameter version)](https://huggingface.co/gpt2-large), [GPT-Medium (the 355M parameter version)](https://huggingface.co/gpt2-medium) and [GPT-2 (the 124M parameter version)](https://huggingface.co/gpt2)
- **Resources for more information:**
- [Research Paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) (see, in particular, the section beginning on page 12 about Automated ML-based detection).
- [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector)
- [OpenAI Blog Post](https://openai.com/blog/gpt-2-1-5b-release/)
- [Explore the detector model here](https://huggingface.co/openai-detector )
## Uses
#### Direct Use
The model is a classifier that can be used to detect text generated by GPT-2 models. However, it is strongly suggested not to use it as a ChatGPT detector for the purposes of making grave allegations of academic misconduct against undergraduates and others, as this model might give inaccurate results in the case of ChatGPT-generated input.
#### Downstream Use
The model's developers have stated that they developed and released the model to help with research related to synthetic text generation, so the model could potentially be used for downstream tasks related to synthetic text generation. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further discussion.
#### Misuse and Out-of-scope Use
The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model developers discuss the risk of adversaries using the model to better evade detection in their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), suggesting that using the model for evading detection or for supporting efforts to evade detection would be a misuse of the model.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
#### Risks and Limitations
In their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), the model developers discuss the risk that the model may be used by bad actors to develop capabilities for evading detection, though one purpose of releasing the model is to help improve detection research.
In a related [blog post](https://openai.com/blog/gpt-2-1-5b-release/), the model developers also discuss the limitations of automated methods for detecting synthetic text and the need to pair automated detection tools with other, non-automated approaches. They write:
> We conducted in-house detection research and developed a detection model that has detection rates of ~95% for detecting 1.5B GPT-2-generated text. We believe this is not high enough accuracy for standalone detection and needs to be paired with metadata-based approaches, human judgment, and public education to be more effective.
The model developers also [report](https://openai.com/blog/gpt-2-1-5b-release/) finding that classifying content from larger models is more difficult, suggesting that detection with automated tools like this model will be increasingly difficult as model sizes increase. The authors find that training detector models on the outputs of larger models can improve accuracy and robustness.
#### Bias
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by RoBERTa base and GPT-2 1.5B (which this model is built/fine-tuned on) can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups (see the [RoBERTa base](https://huggingface.co/roberta-base) and [GPT-2 XL](https://huggingface.co/gpt2-xl) model cards for more information). The developers of this model discuss these issues further in their [paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf).
## Training
#### Training Data
The model is a sequence classifier based on RoBERTa base (see the [RoBERTa base model card](https://huggingface.co/roberta-base) for more details on the RoBERTa base training data) and then fine-tuned using the outputs of the 1.5B GPT-2 model (available [here](https://github.com/openai/gpt-2-output-dataset)).
#### Training Procedure
The model developers write that:
> We based a sequence classifier on RoBERTaBASE (125 million parameters) and fine-tuned it to classify the outputs from the 1.5B GPT-2 model versus WebText, the dataset we used to train the GPT-2 model.
They later state:
> To develop a robust detector model that can accurately classify generated texts regardless of the sampling method, we performed an analysis of the model’s transfer performance.
See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further details on the training procedure.
## Evaluation
The following evaluation information is extracted from the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf).
#### Testing Data, Factors and Metrics
The model is intended to be used for detecting text generated by GPT-2 models, so the model developers test the model on text datasets, measuring accuracy by:
> testing 510-token test examples comprised of 5,000 samples from the WebText dataset and 5,000 samples generated by a GPT-2 model, which were not used during the training.
#### Results
The model developers [find](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf):
> Our classifier is able to detect 1.5 billion parameter GPT-2-generated text with approximately 95% accuracy...The model’s accuracy depends on sampling methods used when generating outputs, like temperature, Top-K, and nucleus sampling ([Holtzman et al., 2019](https://arxiv.org/abs/1904.09751). Nucleus sampling outputs proved most difficult to correctly classify, but a detector trained using nucleus sampling transfers well across other sampling methods. As seen in Figure 1 [in the paper], we found consistently high accuracy when trained on nucleus sampling.
See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), Figure 1 (on page 14) and Figure 2 (on page 16) for full results.
## Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** Unknown
- **Hours used:** Unknown
- **Cloud Provider:** Unknown
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications
The model developers write that:
See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further details on the modeling architecture and training details.
## Citation Information
```bibtex
@article{solaiman2019release,
title={Release strategies and the social impacts of language models},
author={Solaiman, Irene and Brundage, Miles and Clark, Jack and Askell, Amanda and Herbert-Voss, Ariel and Wu, Jeff and Radford, Alec and Krueger, Gretchen and Kim, Jong Wook and Kreps, Sarah and others},
journal={arXiv preprint arXiv:1908.09203},
year={2019}
}
```
APA:
- Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., ... & Wang, J. (2019). Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203.
## Model Card Authors
This model card was written by the team at Hugging Face.
## How to Get Started with the Model
This model can be instantiated and run with a Transformers pipeline:
```python
from transformers import pipeline
pipe = pipeline("text-classification", model="roberta-base-openai-detector")
print(pipe("Hello world! Is this content AI-generated?")) # [{'label': 'Real', 'score': 0.8036582469940186}]
``` |
2 | roberta-large-mnli | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language:
- en
license: mit
tags:
- autogenerated-modelcard
datasets:
- multi_nli
- wikipedia
- bookcorpus
---
# roberta-large-mnli
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Citation Information](#citation-information)
- [Model Card Authors](#model-card-author)
## Model Details
**Model Description:** roberta-large-mnli is the [RoBERTa large model](https://huggingface.co/roberta-large) fine-tuned on the [Multi-Genre Natural Language Inference (MNLI)](https://huggingface.co/datasets/multi_nli) corpus. The model is a pretrained model on English language text using a masked language modeling (MLM) objective.
- **Developed by:** See [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta) for model developers
- **Model Type:** Transformer-based language model
- **Language(s):** English
- **License:** MIT
- **Parent Model:** This model is a fine-tuned version of the RoBERTa large model. Users should see the [RoBERTa large model card](https://huggingface.co/roberta-large) for relevant information.
- **Resources for more information:**
- [Research Paper](https://arxiv.org/abs/1907.11692)
- [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta)
## How to Get Started with the Model
Use the code below to get started with the model. The model can be loaded with the zero-shot-classification pipeline like so:
```python
from transformers import pipeline
classifier = pipeline('zero-shot-classification', model='roberta-large-mnli')
```
You can then use this pipeline to classify sequences into any of the class names you specify. For example:
```python
sequence_to_classify = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(sequence_to_classify, candidate_labels)
```
## Uses
#### Direct Use
This fine-tuned model can be used for zero-shot classification tasks, including zero-shot sentence-pair classification (see the [GitHub repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta) for examples) and zero-shot sequence classification.
#### Misuse and Out-of-scope Use
The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). The [RoBERTa large model card](https://huggingface.co/roberta-large) notes that: "The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral."
Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example:
```python
sequence_to_classify = "The CEO had a strong handshake."
candidate_labels = ['male', 'female']
hypothesis_template = "This text speaks about a {} profession."
classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template)
```
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
## Training
#### Training Data
This model was fine-tuned on the [Multi-Genre Natural Language Inference (MNLI)](https://cims.nyu.edu/~sbowman/multinli/) corpus. Also see the [MNLI data card](https://huggingface.co/datasets/multi_nli) for more information.
As described in the [RoBERTa large model card](https://huggingface.co/roberta-large):
> The RoBERTa model was pretrained on the reunion of five datasets:
>
> - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books;
> - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ;
> - [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news articles crawled between September 2016 and February 2019.
> - [OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to train GPT-2,
> - [Stories](https://arxiv.org/abs/1806.02847), a dataset containing a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas.
>
> Together theses datasets weight 160GB of text.
Also see the [bookcorpus data card](https://huggingface.co/datasets/bookcorpus) and the [wikipedia data card](https://huggingface.co/datasets/wikipedia) for additional information.
#### Training Procedure
##### Preprocessing
As described in the [RoBERTa large model card](https://huggingface.co/roberta-large):
> The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,000. The inputs of
> the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
> with `<s>` and the end of one by `</s>`
>
> The details of the masking procedure for each sentence are the following:
> - 15% of the tokens are masked.
> - In 80% of the cases, the masked tokens are replaced by `<mask>`.
> - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
> - In the 10% remaining cases, the masked tokens are left as is.
>
> Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
##### Pretraining
Also as described in the [RoBERTa large model card](https://huggingface.co/roberta-large):
> The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. The
> optimizer used is Adam with a learning rate of 4e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and
> \\(\epsilon = 1e-6\\), a weight decay of 0.01, learning rate warmup for 30,000 steps and linear decay of the learning
> rate after.
## Evaluation
The following evaluation information is extracted from the associated [GitHub repo for RoBERTa](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta).
#### Testing Data, Factors and Metrics
The model developers report that the model was evaluated on the following tasks and datasets using the listed metrics:
- **Dataset:** Part of [GLUE (Wang et al., 2019)](https://arxiv.org/pdf/1804.07461.pdf), the General Language Understanding Evaluation benchmark, a collection of 9 datasets for evaluating natural language understanding systems. Specifically, the model was evaluated on the [Multi-Genre Natural Language Inference (MNLI)](https://cims.nyu.edu/~sbowman/multinli/) corpus. See the [GLUE data card](https://huggingface.co/datasets/glue) or [Wang et al. (2019)](https://arxiv.org/pdf/1804.07461.pdf) for further information.
- **Tasks:** NLI. [Wang et al. (2019)](https://arxiv.org/pdf/1804.07461.pdf) describe the inference task for MNLI as:
> The Multi-Genre Natural Language Inference Corpus [(Williams et al., 2018)](https://arxiv.org/abs/1704.05426) is a crowd-sourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. We use the standard test set, for which we obtained private labels from the authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) sections. We also use and recommend the SNLI corpus [(Bowman et al., 2015)](https://arxiv.org/abs/1508.05326) as 550k examples of auxiliary training data.
- **Metrics:** Accuracy
- **Dataset:** [XNLI (Conneau et al., 2018)](https://arxiv.org/pdf/1809.05053.pdf), the extension of the [Multi-Genre Natural Language Inference (MNLI)](https://cims.nyu.edu/~sbowman/multinli/) corpus to 15 languages: English, French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. See the [XNLI data card](https://huggingface.co/datasets/xnli) or [Conneau et al. (2018)](https://arxiv.org/pdf/1809.05053.pdf) for further information.
- **Tasks:** Translate-test (e.g., the model is used to translate input sentences in other languages to the training language)
- **Metrics:** Accuracy
#### Results
GLUE test results (dev set, single model, single-task fine-tuning): 90.2 on MNLI
XNLI test results:
| Task | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur |
|:----:|:--:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| |91.3|82.91|84.27|81.24|81.74|83.13|78.28|76.79|76.64|74.17|74.05| 77.5| 70.9|66.65|66.81|
## Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type and hours used based on the [associated paper](https://arxiv.org/pdf/1907.11692.pdf).
- **Hardware Type:** 1024 V100 GPUs
- **Hours used:** 24 hours (one day)
- **Cloud Provider:** Unknown
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications
See the [associated paper](https://arxiv.org/pdf/1907.11692.pdf) for details on the modeling architecture, objective, compute infrastructure, and training details.
## Citation Information
```bibtex
@article{liu2019roberta,
title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach},
author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and
Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and
Luke Zettlemoyer and Veselin Stoyanov},
journal={arXiv preprint arXiv:1907.11692},
year = {2019},
}
``` |
8 | AIDA-UPM/bertweet-base-multi-mami | [
"misogynous",
"objectification",
"shaming",
"stereotype",
"violence"
] | ---
pipeline_tag: text-classification
tags:
- text-classification
- misogyny
language: en
license: apache-2.0
widget:
- text: "Women wear yoga pants because men don't stare at their personality"
example_title: "Misogyny detection"
---
# bertweet-base-multi-mami
This is a Bertweet model: It maps sentences & paragraphs to a 768 dimensional dense vector space and classifies them into 5 multi labels.
# Multilabels
label2id={
"misogynous": 0,
"shaming": 1,
"stereotype": 2,
"objectification": 3,
"violence": 4,
},
|
9 | ASCCCCCCCC/PENGMENGJIE-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
model_index:
- name: PENGMENGJIE-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkown 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: 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
### Framework versions
- Transformers 4.9.0
- Pytorch 1.7.1+cpu
- Datasets 1.17.0
- Tokenizers 0.10.3
|
10 | ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh_20000 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-chinese-finetuned-amazon_zh_20000
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-base-chinese-finetuned-amazon_zh_20000
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: 1.1683
- Accuracy: 0.5224
- F1: 0.5194
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.2051 | 1.0 | 2500 | 1.1717 | 0.506 | 0.4847 |
| 1.0035 | 2.0 | 5000 | 1.1683 | 0.5224 | 0.5194 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
|
11 | ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-chinese-amazon_zh_20000
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-chinese-amazon_zh_20000
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: 1.1518
- Accuracy: 0.5092
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.196 | 1.0 | 1250 | 1.1518 | 0.5092 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
|
12 | ASCCCCCCCC/distilbert-base-multilingual-cased-amazon_zh_20000 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-multilingual-cased-amazon_zh_20000
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-multilingual-cased-amazon_zh_20000
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3031
- Accuracy: 0.4406
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.396 | 1.0 | 1250 | 1.3031 | 0.4406 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
|
13 | ASCCCCCCCC/distilbert-base-uncased-finetuned-amazon_zh_20000 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-amazon_zh_20000
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-amazon_zh_20000
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: 1.3516
- Accuracy: 0.414
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4343 | 1.0 | 1250 | 1.3516 | 0.414 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
|
14 | ASCCCCCCCC/distilbert-base-uncased-finetuned-clinc | [
"accept_reservations",
"account_blocked",
"alarm",
"application_status",
"apr",
"are_you_a_bot",
"balance",
"bill_balance",
"bill_due",
"book_flight",
"book_hotel",
"calculator",
"calendar",
"calendar_update",
"calories",
"cancel",
"cancel_reservation",
"car_rental",
"card_declined",
"carry_on",
"change_accent",
"change_ai_name",
"change_language",
"change_speed",
"change_user_name",
"change_volume",
"confirm_reservation",
"cook_time",
"credit_limit",
"credit_limit_change",
"credit_score",
"current_location",
"damaged_card",
"date",
"definition",
"direct_deposit",
"directions",
"distance",
"do_you_have_pets",
"exchange_rate",
"expiration_date",
"find_phone",
"flight_status",
"flip_coin",
"food_last",
"freeze_account",
"fun_fact",
"gas",
"gas_type",
"goodbye",
"greeting",
"how_busy",
"how_old_are_you",
"improve_credit_score",
"income",
"ingredient_substitution",
"ingredients_list",
"insurance",
"insurance_change",
"interest_rate",
"international_fees",
"international_visa",
"jump_start",
"last_maintenance",
"lost_luggage",
"make_call",
"maybe",
"meal_suggestion",
"meaning_of_life",
"measurement_conversion",
"meeting_schedule",
"min_payment",
"mpg",
"new_card",
"next_holiday",
"next_song",
"no",
"nutrition_info",
"oil_change_how",
"oil_change_when",
"oos",
"order",
"order_checks",
"order_status",
"pay_bill",
"payday",
"pin_change",
"play_music",
"plug_type",
"pto_balance",
"pto_request",
"pto_request_status",
"pto_used",
"recipe",
"redeem_rewards",
"reminder",
"reminder_update",
"repeat",
"replacement_card_duration",
"report_fraud",
"report_lost_card",
"reset_settings",
"restaurant_reservation",
"restaurant_reviews",
"restaurant_suggestion",
"rewards_balance",
"roll_dice",
"rollover_401k",
"routing",
"schedule_maintenance",
"schedule_meeting",
"share_location",
"shopping_list",
"shopping_list_update",
"smart_home",
"spelling",
"spending_history",
"sync_device",
"taxes",
"tell_joke",
"text",
"thank_you",
"time",
"timer",
"timezone",
"tire_change",
"tire_pressure",
"todo_list",
"todo_list_update",
"traffic",
"transactions",
"transfer",
"translate",
"travel_alert",
"travel_notification",
"travel_suggestion",
"uber",
"update_playlist",
"user_name",
"vaccines",
"w2",
"weather",
"what_are_your_hobbies",
"what_can_i_ask_you",
"what_is_your_name",
"what_song",
"where_are_you_from",
"whisper_mode",
"who_do_you_work_for",
"who_made_you",
"yes"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
model_index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkown 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: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.9.0
- Pytorch 1.7.1+cpu
- Datasets 1.17.0
- Tokenizers 0.10.3
|
15 | AWTStress/stress_classifier | [
"Emotional Turmoil",
"Everyday Decision Making",
"Family Issues",
"Financial Problem",
"Health, Fatigue, or Physical Pain",
"Other",
"School",
"Social Relationships",
"Work"
] | ---
tags:
- generated_from_keras_callback
model-index:
- name: tmp_znj9o4r
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tmp_znj9o4r
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
16 | AWTStress/stress_score | [
"LABEL_0"
] | ---
tags:
- generated_from_keras_callback
model-index:
- name: stress_score
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# stress_score
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
17 | Abirate/bert_fine_tuned_cola | [
"acceptable",
"unacceptable"
] |
## Petrained Model BERT: base model (cased)
BERT base model (cased) is a 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 case-sensitive: it makes a difference between english and English.
## Pretained Model Description
BERT is an auto-encoder transformer model pretrained on a large corpus of English data (English Wikipedia + Books Corpus) in a self-supervised fashion. This means the targets are computed from the inputs themselves, and humans are not needed to label the data. It was pretrained with two objectives:
- Masked language modeling (MLM)
- Next sentence prediction (NSP)
## Fine-tuned Model Description: BERT fine-tuned Cola
The pretrained model could be fine-tuned on other NLP tasks. The BERT model has been fine-tuned on a cola dataset from the GLUE BENCHAMRK, which is an academic benchmark that aims to measure the performance of ML models. Cola is one of the 11 datasets in this GLUE BENCHMARK.
By fine-tuning BERT on cola dataset, the model is now able to classify a given setence gramatically and semantically as acceptable or not acceptable
## How to use ?
###### Directly with a pipeline for a text-classification NLP task
```python
from transformers import pipeline
cola = pipeline('text-classification', model='Abirate/bert_fine_tuned_cola')
cola("Tunisia is a beautiful country")
[{'label': 'acceptable', 'score': 0.989352285861969}]
```
###### Breaking down all the steps (Tokenization, Modeling, Postprocessing)
```python
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
import tensorflow as tf
import numpy as np
tokenizer = AutoTokenizer.from_pretrained('Abirate/bert_fine_tuned_cola')
model = TFAutoModelForSequenceClassification.from_pretrained("Abirate/bert_fine_tuned_cola")
text = "Tunisia is a beautiful country."
encoded_input = tokenizer(text, return_tensors='tf')
#The logits
output = model(encoded_input)
#Postprocessing
probas_output = tf.math.softmax(tf.squeeze(output['logits']), axis = -1)
class_preds = np.argmax(probas_output, axis = -1)
#Predicting the class acceptable or not acceptable
model.config.id2label[class_preds]
#Result
'acceptable'
``` |
18 | ActivationAI/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.928
- name: F1
type: f1
value: 0.9280065074208208
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2128
- Accuracy: 0.928
- F1: 0.9280
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8151 | 1.0 | 250 | 0.3043 | 0.907 | 0.9035 |
| 0.24 | 2.0 | 500 | 0.2128 | 0.928 | 0.9280 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
65 | Adi2K/Priv-Consent | [
"CON",
"NOT"
] | ---
language: eng
widget:
- text: "You can control cookies and tracking tools. To learn how to manage how we - and our vendors - use cookies and other tracking tools, please click here."
datasets:
- Adi2K/autonlp-data-Priv-Consent
---
# Model
- Problem type: Binary Classification
- Model ID: 12592372
## Validation Metrics
- Loss: 0.23033875226974487
- Accuracy: 0.9138655462184874
- Precision: 0.9087136929460581
- Recall: 0.9201680672268907
- AUC: 0.9690346726926065
- F1: 0.9144050104384133
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/Adi2K/autonlp-Priv-Consent-12592372
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Adi2K/autonlp-Priv-Consent-12592372", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Adi2K/autonlp-Priv-Consent-12592372", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
66 | AhmedBou/TuniBert | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
language:
- ar
tags:
- sentiment analysis
- classification
- arabic dialect
- tunisian dialect
---
This is a fineTued Bert model on Tunisian dialect text (Used dataset: AhmedBou/Tunisian-Dialect-Corpus), ready for sentiment analysis and classification tasks.
LABEL_1: Positive
LABEL_2: Negative
LABEL_0: Neutral
This work is an integral component of my Master's degree thesis and represents the culmination of extensive research and labor.
If you wish to utilize the Tunisian-Dialect-Corpus or the TuniBert model, kindly refer to the directory provided. [huggingface.co/AhmedBou][github.com/BoulahiaAhmed] |
67 | Aimendo/autonlp-triage-35248482 | [
"acknowledgement",
"ads",
"approval",
"away",
"cancellation",
"doc_request",
"inquirey",
"modification",
"new_booking",
"refund"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Aimendo/autonlp-data-triage
co2_eq_emissions: 7.989144645413398
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 35248482
- CO2 Emissions (in grams): 7.989144645413398
## Validation Metrics
- Loss: 0.13783401250839233
- Accuracy: 0.9728654124457308
- Macro F1: 0.949537871674076
- Micro F1: 0.9728654124457308
- Weighted F1: 0.9732422812610365
- Macro Precision: 0.9380372699332605
- Micro Precision: 0.9728654124457308
- Weighted Precision: 0.974548513256663
- Macro Recall: 0.9689346153591594
- Micro Recall: 0.9728654124457308
- Weighted Recall: 0.9728654124457308
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/Aimendo/autonlp-triage-35248482
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Aimendo/autonlp-triage-35248482", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Aimendo/autonlp-triage-35248482", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
68 | Ajay191191/autonlp-Test-530014983 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Ajay191191/autonlp-data-Test
co2_eq_emissions: 55.10196329868386
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 530014983
- CO2 Emissions (in grams): 55.10196329868386
## Validation Metrics
- Loss: 0.23171618580818176
- Accuracy: 0.9298837645294338
- Precision: 0.9314414866901055
- Recall: 0.9279459594696022
- AUC: 0.979447403984557
- F1: 0.9296904373981703
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/Ajay191191/autonlp-Test-530014983
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Ajay191191/autonlp-Test-530014983", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Ajay191191/autonlp-Test-530014983", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
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