# DistilBERT on ArXiv

This model was developed to predict the top-level category of a paper, given the
paper's abstract, title, and list of authors. It was trained over a subset of
data pulled from the ArXiv API.


{"language": "en", "license": "apache-2.0", "tags": ["arxiv", "topic-classification", "distilbert"], "widget": [{"text": "Title: ImageNet classification with deep convolutional neural networks\n Abstract: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2\\% achieved by the second-best entry.\n Authors: A. Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton"}, {"text": "Title: The Design of Radio Telescope Array Configurations using Multiobjective\n  Optimization: Imaging Performance versus Cable Length\nAbstract: The next generation of radio telescope interferometric arrays requires\ncareful design of the array configuration to optimize the performance of the\noverall system. We have developed a framework, based on a genetic algorithm,\nfor rapid exploration and optimization of the objective space pertaining to\nmultiple objectives. We have evaluated a large space of possible designs for\n27-, 60-, 100-, and 160-station arrays. The 27-station optimizations can be\ncompared to the well-known VLA case, and the larger array designs apply to\narrays currently under design such as LOFAR, ATA, and the SKA. In the initial\nimplementation of our framework we evaluate designs with respect to two\nmetrics, array imaging performance and the length of cable necessary to connect\nthe stations. Imaging performance is measured by the degree to which the\nsampling of the uv plane is uniform. For the larger arrays we find that\nwell-known geometric designs perform well and occupy the Pareto front of\noptimum solutions. For the 27-element case we find designs, combining features\nof the well-known designs, that are more optimal as measured by these two\nmetrics. The results obtained by the multiobjective genetic optimization are\ncorroborated by simulated annealing, which also reveals the role of entropy in\narray optimization. Our framework is general, and may be applied to other\ndesign goals and issues, such as particular schemes for sampling the uv plane,\narray robustness, and phased deployment of arrays.\nAuthors: Babak E. Cohanim, Jacqueline N. Hewitt, Olivier de Weck"}, {"text": "Title: Evidence for a Neutron Star in the non-pulsating massive X-ray binary\n  4U2206+54\nAbstract: We present an analysis of archival RXTE and BeppoSAX data of the X-ray source\n4U2206+54 . For the first time, high energy data (> 30 kev) are analyzed for\nthis source. The data are well described by comptonization models (CompTT and\nBMC) in which seed photons with temperatures between 1.1 kev and 1.5 kev are\ncomptonized by a hot plasma at 50 kev thereby producing a hard tail which\nextends up to, at least, 100 kev. We offer a new method of identification of\nneutron star systems using a temperature - luminosity relation. If a given\nX-ray source is characterized by a low bolometric luminosity and a relatively\nhigh color blackbody temperature (>1 kev) it has necessarily to be a neutron\nstar rather than a black hole. From these arguments it is shown that the area\nof the soft photon source must be small (r ~ 1 km) and that the accretion disk,\nif present, must be truncated very far from the compact object. Here we report\non the possible existence of a cyclotron line around 30 kev. The presence of a\nneutron star in the system is strongly favored by the available data.\nAuthors: J. M. Torrej\u00f3n, I. Kreykenbohm, A. Orr, L. Titarchuk, I. Negueruela"}, {"text": "Title: Solving the Schrodinger Equation for a Quantum Well\n  with a Non-Uniform Potential\nAbstract: We present a numerical solution to the Schrodinger equation for a\nquantum well with a non-uniform potential. The potential is a Gaussian\nwith a non-uniform distribution of energies. The solution is a solution to the\nSchrodinger equation with a non-uniform potential. The solution is a\nnon-uniform solution to the Schrodinger equation with a non-uniform potential.\nAuthors: George K. Kostopoulos, John A. Kostopoulos, and John C. Kostopoulos"}, {"text": "Title: Inverting Black-Scholes Model for Option Pricing\n  with a Non-Uniformly Distributed Risk\nAbstract: We present a numerical solution to the Black-Scholes model for\noption pricing with a non-uniformly distributed risk. The solution is a\nnon-uniform solution to the Black-Scholes model with a non-uniformly\ndistributed risk. The solution is a non-uniform solution to the\nBlack-Scholes model with a non-uniformly distributed risk.\nAuthors: Z. Starosov, L. Randhawa"}]}
transformers pytorch distilbert text-classification arxiv topic-classification en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #arxiv #topic-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

# DistilBERT on ArXiv

This model was developed to predict the top-level category of a paper, given the
paper's abstract, title, and list of authors. It was trained over a subset of
data pulled from the ArXiv API.


# DistilBERT on ArXiv

This model was developed to predict the top-level category of a paper, given the
paper's abstract, title, and list of authors. It was trained over a subset of
data pulled from the ArXiv API.
TAGS
#transformers #pytorch #distilbert #text-classification #arxiv #topic-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # DistilBERT on ArXiv

This model was developed to predict the top-level category of a paper, given the
paper's abstract, title, and list of authors. It was trained over a subset of
data pulled from the ArXiv API.
passage: TAGS
#transformers #pytorch #distilbert #text-classification #arxiv #topic-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# DistilBERT on ArXiv

This model was developed to predict the top-level category of a paper, given the
paper's abstract, title, and list of authors. It was trained over a subset of
data pulled from the ArXiv API.