The massive MIMO approach presents an exciting prospect for the upcoming generation of wireless transmission systems. However, the adoption of actual massive MIMO scenarios is hindered by high hardware expenses and increased energy usage, particularly as the quantity of RF modules expands. To address this issue and make massive MIMO more commercially viable, the design of 1-bit analog-to-digital converters (ADCs) has been considered as a solution. Various deep learning (DL) techniques for channel estimation (CE) with 1-bit ADCs have been developed in the literature. Nonetheless, most of these methods demonstrate limited performance in CE regarding pilot lengths and noise levels. In this paper, an efficient DL model known as bi-directional long short-term memory (BiLSTM) is proposed. This model enhances CE performance with limited pilot signals by training on long input sequence data within a bi-directional framework. The bi-directional (forward and backward) tasks in the hidden layers of BiLSTM contribute to its enhanced training ability, thereby enriching the CE of the proposed system. Moreover, in this paradigm, BiLSTM is utilized in conjunction with previous channel estimation data to learn the complex mapping from quantized incoming evaluations to channels. Consequently, the proposed model demonstrates superior CE efficiency for the same size of pilot sequencing as it deduces the necessary length and configuration of the pilot sequencing to ensure the existence of this mapping function. Therefore, lower pilot signals are needed with additional antennas for identical CE capability. Simulation outcomes verify that the proposed model exhibits satisfactory CE accuracy. It is confirmed that the increase of the number of antennas improves CE concerning the acquired signal-to-noise ratio per antenna and the normalized mean squared error.